Method and composition for diagnosis of aggressive prostate cancer

ABSTRACT

Techniques for diagnosis of aggressive prostate cancer include determining a level of expression of each of the genes encoding (FOXM1) Forkhead box protein M1 and Centromere protein F (CENPF) in a test sample. If the level of expression of each of the FOXM1 and CENPF genes in the test sample is at least 35% higher than the corresponding level in a control sample, then it is determined that the subject has an aggressive form of prostate cancer or has a high risk of prostate cancer progressing to an aggressive form. Alternatively, if at least 50% of prostate cancer cells in the sample express both FOXM1 protein and CENPF protein at a composite score of at least 100 for each, then the above diagnosis is made. Composite score is calculated by multiplying a percent staining value by a staining intensity value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of Provisional Appln. 61/966,271, filedFeb. 19, 2014 under 35 U.S.C. §119(e).

STATEMENT OF GOVERNMENTAL INTEREST

This invention was made with Government support under CA084294, U54CA121852 and CA154293 awarded by the National Institutes of Health. TheGovernment has certain rights in the invention.

BACKGROUND

Cancer is not a single entity but rather a highly individualizedspectrum of diseases characterized by a number of genetic and genomicalterations (Hanahan and Weinberg, 2011). Distinguishing molecularalterations that constitute true drivers of cancer phenotypes from themultitude that are simply de-regulated has proven to be a daunting task,which is further exacerbated by the complexity of elucidating how suchdrivers interact synergistically to elicit cancer phenotypes. Prostatecancer is particularly challenging because its notorious heterogeneity,combined with a relative paucity of recurrent gene mutations, has madeprostate cancer especially difficult to identify molecularly distinctsubtypes with known clinical outcomes (Baca et al., 2013; Schoenborn etal., 2013; Shen and Abate-Shen, 2010). Additionally, while early-stageprostate tumors are readily treatable (Cooperberg et al., 2007),advanced prostate cancer frequently progresses to castration-resistantdisease, which is often metastatic and nearly always fatal (Ryan andTindall, 2011; Scher and Sawyers, 2005).

It should be noted that several factors, including an increase in theaging population and widespread screening for prostate specific antigen(PSA), have contributed to a substantial rise in diagnoses of prostatecancer. The primary means of determining the appropriate treatmentcourse for men diagnosed with prostate cancer still relies on Gleasongrading, a histopathological evaluation that lacks a precise molecularcorrelate. While patients with high Gleason score (Gleason ≧8) tumorsare recommended to undergo immediate treatment, the appropriatetreatment for those with low (Gleason 6) or intermediate (Gleason 7)Gleason score tumors remains more ambiguous. Indeed, although themajority of Gleason grade 6 tumors, as well as many Gleason grade 7tumors, are likely to remain indolent (i.e., low-risk, non-aggressive ornon-invasive), a minority (˜10%) will progress to aggressive disease.

Indeed, the current lack of reliable and reproducible assays to identifytumors destined to remain indolent versus those that are aggressive, hasresulted in substantial overtreatment of patients that would not die ofthe disease if left untreated. Consequently, “active surveillance” hasemerged as an alternative for monitoring men with indolent prostatecancer, with the goal of avoiding treatment unless there is evidence ofdisease progression. The obvious advantage of active surveillance isthat it avoids overtreatment; however, the potential concern is that itmay miss the opportunity for early intervention for patients withaggressive tumors. Therefore, better methods with a molecular correlatefor diagnosing aggressive prostate cancer have great value.

SUMMARY

Applicants have determined that there is a need to identify moleculardeterminants of cancers, including but not limited to, aggressiveprostate cancer subtypes, a need to identify other prognostic biomarkersof disease outcome, and a need to treat such cancers. The subject matterdisclosed herein addresses this need.

In a first set of embodiments, a method includes obtaining a testprostate cancer sample from a subject having prostate cancer anddetermining a level of expression of each of the genes encoding (FOXM1)Forkhead box protein M1 and Centromere protein F (CENPF) in the testsample and a control sample. The method also includes comparing thelevel of expression of each of the FOXM1 and CENPF genes in the testsample to the corresponding level in the control sample. The methodfurther includes determining that the subject has an aggressive form ofprostate cancer or has a high risk of prostate cancer progressing to anaggressive form, if the level of expression of each of the FOXM1 andCENPF genes in the test sample is at least 35% higher than thecorresponding level in the control sample.

In a second set of embodiments, a method includes obtaining a prostatecancer sample from a subject having prostate cancer, and determining alevel of expression of FOXM1 protein and CENPF protein in the prostatecancer sample by immunostaining with a first antibody that specificallybinds to FOXM1 and a second antibody that specifically binds to CENPF.The method further includes determining that the subject has anaggressive form of prostate cancer or has a high risk of prostate cancerprogressing to an aggressive form, if at least 50% of prostate cancercells in the prostate cancer sample express both FOXM1 protein and CENPFprotein at a composite score of at least 100 for each protein. Thecomposite score is calculated by multiplying a percent staining value bya staining intensity value.

In a third set of embodiments, a method includes obtaining a prostatecancer sample from a subject having prostate cancer (or at risk ofdeveloping prostate cancer). The method also includes applying a firstantibody that specifically binds to FOXM1 protein in the sample, whereinpresence of FOXM1 creates an antibody-FOXM1 complex; and applying asecond antibody that specifically binds to CENPF in the sample, whereinpresence of the CENPF creates an antibody-CENPF complex. The methodfurther includes applying a first detection agent that detects theantibody-FOXM1 complex; and a second detection agent that detects theantibody-CENPF complex. The method still further includes thendetermining that the subject has an aggressive form of prostate canceror has a high risk of prostate cancer progressing to an aggressive form,if at least 50% of prostate cancer cells in the sample express bothFOXM1 protein and CENPF protein at a composite score of at least 100 foreach protein.

In a fourth set of embodiments, a diagnostic kit for detecting anexpression level of an mRNA or a protein encoding FOXM1 or CENPF or bothin a biological sample includes oligonucleotides that specificallyhybridize to each of the respective mRNAs or one or more agents thatspecifically bind to each of the respective proteins, or both.

In a fifth set of embodiments, a microarray includes a plurality ofoligonucleotides that specifically hybridize to an mRNA encoded by eachof the FOXM1 or CENPF genes, which oligonucleotides are fixed on themicroarray.

In a sixth set of embodiments, a microarray includes a plurality ofantibodies or antibody fragments that specifically bind to either orboth of FOXM1 protein or CENPF protein or biologically active fragmentthereof, which antibodies or antibody fragments are fixed on themicroarray.

Still other aspects, features, and advantages of the invention arereadily apparent from the following detailed description, simply byillustrating a number of particular embodiments and implementations,including the best mode contemplated for carrying out the invention. Theinvention is also capable of other and different embodiments, and itsseveral details can be modified in various obvious respects, all withoutdeparting from the spirit and scope of the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1A is a block diagram and graph that illustrate example geneprofiling data for multiple human subjects with various stages ofprostate cancer, according to an embodiment;

FIG. 1B is a block diagram and graph that illustrate example geneprofiling data for multiple mouse models for prostate cancer, accordingto an embodiment;

FIG. 1C is a block diagram and graph that illustrate example effects ongene profiling data for mouse models in response to variousperturbagens, according to an embodiment;

FIG. 2A is a block diagram and graph that illustrate exampleinteractomes for human and mouse models with prostate cancer, accordingto an embodiment;

FIG. 2B is a graph that illustrates example percentage of theinteractomes that are conserved between human and mouse models withprostate cancer, according to an embodiment;

FIG. 3A is a Venn diagram and table that illustrate example selection ofa subset of master regulators from a full set determined by availableautomated computer processes, according to an embodiment;

FIG. 3B is a diagram that illustrates example ranking of masterregulators for their impacts on prostate cancer, according to anembodiment;

FIG. 3C is a table that illustrates example ranking of master regulatorsfor their impacts on prostate cancer by various available algorithms,according to an embodiment;

FIG. 4 is a table that illustrates example predicted synergy of FOXM1And CENPF among the subset of master regulators using availablealgorithms, according to an embodiment;

FIG. 5 is a table that illustrates example clinical datasets used todetermine whether synergistic master regulators FOXM1 and CENPF areprognostic biomarkers of prostate cancer outcomes, according to anembodiment;

FIG. 6A is an image that illustrates example micrographs of FOXM1 andCENPF stained tissues showing enhanced concentrations of both inaggressive prostate cancer tumors compared to other prostate tumors,according to an embodiment;

FIG. 6B is an image that illustrates example micrographs of FOXM1 andCENPF stained tissues showing enhanced concentrations of both inmetastasized lung and liver tumors, according to an embodiment;

FIG. 6C through FIG. 6E are graphs that illustrate example Kaplan-Meiersurvival analysis based on protein expression levels of FOXM1 and CENPFwith respect to time to biochemical recurrence, time to prostatecancer-specific death, or time to metastatic progression, respectively,according to an embodiment;

FIG. 6F and FIG. 6G are graphs that illustrate example Kaplan-Meiersurvival analysis based on the interactome-inferred activity levels ofFOXM1 and CENPF with respect to time to biochemical recurrence, or timeto prostate cancer-specific death, respectively, according to anembodiment;

FIG. 7 is a table that illustrates example prognostic power ofco-expression of protein levels of FOXM1 and CENPF, with death due toprostate cancer and time to metastasis as evaluation endpoints,according to an embodiment;

FIG. 8 is a flow chart that illustrates an example diagnostic method fordetermining whether a subjects is at risk based on coexpression of thesynergistic master regulators FOXM1 and CENPF, according to anembodiment;

FIG. 9A is a graph that illustrates example resulting relative mRNAexpression levels for the shared targets of FOXM1 and CENPF in theindicated cell lines following individual or co-silencing of FOXM1 andCENPF, according to an embodiment;

FIG. 9B is a graph that illustrates example enrichment of FOXM1 bindingnormalized to input with and without silencing of CENPF, according to anembodiment;

FIG. 9C is an image of micrographs that illustrate example changes insubcellular localization of FOXM1 and CENPF proteins in prostate cancercells after silencing either, according to an embodiment; and

FIG. 10 is a flow chart that illustrates an example method fordetermining coexpression of the synergistic master regulators FOXM1 andCENPF, according to an embodiment.

DEFINITIONS

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. The practice of the presentinvention will employ, unless indicated specifically to the contrary,conventional methods of molecular biology and recombinant DNA techniqueswithin the skill of the art, many of which are described below for thepurpose of illustration. Such techniques are fully explained in theliterature. See, e.g., Singleton et al., Dictionary of Microbiology andMolecular Biology 3rd.sup.ed., J. Wiley & Sons (2001); March, AdvancedOrganic Chemistry Reactions, Mechanisms and Structure 5th.sup.ed., J.Wiley & Sons (2001); Sambrook & Russell, eds., Molecular Cloning: ALaboratory Manual 3rd ed., Cold Spring Harbor Laboratory Press (2001);Glover, ed., DNA Cloning: A Practical Approach, vol. I & II (2002);Gait, ed., Oligonucleotide Synthesis: A practical approach, OxfordUniversity Press (1984); Herdewijn, ed., Oligonucleotide Synthesis:Methods and Applications, Humana Press (2005); Hames & Higgins, eds.,Nucleic Acid Hybridisation: A Practical Approach, IRL Press (1985);Buzdin & Lukyanov, eds., Nucleic Acid Hybridization: ModernApplications, Springer (2007); Hames & Higgins, eds., Transcription andTranslation: A Practical Approach, IRL Press (1984); Freshney, ed.,Animal Cell Culture, Oxford UP (1986); Freshney, Culture of AnimalCells: A Manual of Basic Technique and Specialized Applications, 6thed., John Wiley & Sons (2010); Perbal, A Practical Guide to MolecularCloning, 3rd ed., Wiley-Liss (2014); Farrell, RNA Methodologies: ALaboratory Guide for Isolation and Characterization, 3rd ed.,Elsevier/Focal Press (2005); Lilley & Dahlberg, eds., Methods inEnzymology: DNA Structures, Part A: Synthesis and Physical Analysis ofDNA, Academic Press (1992); Harlow & Lane, Using Antibodies: ALaboratory Manual: Portable Protocol no. 1, Cold Spring HarborLaboratory Press (1999); Harlow & Lane, eds., Antibodies: A LaboratoryManual, Cold Spring Harbor Laboratory Press (1988); Seethala &Fernandes, eds., Handbook of Drug Screening, Marcel Dekker (2001); andRoskams & Rodgers, eds., Lab Ref: A Handbook of Recipes, Reagents, andOther Reference Tools for Use at the Bench, Cold Spring HarborLaboratory (2002) provide one skilled in the art with a general guide tomany of the terms used in the present application.

One skilled in the art will recognize many methods and materials similaror equivalent to those described herein, which could be used in thepractice of the present invention. Other features and advantages of theinvention will become apparent from the following detailed description,taken in conjunction with the accompanying drawings, which illustrate,by way of example, various features of embodiments of the invention.Indeed, the present invention is in no way limited to the methods andmaterials described. For convenience, certain terms employed herein inthe specification, examples and appended claims are collected here.

Unless stated otherwise, or implicit from context, the following termsand phrases include the meanings provided below. Unless explicitlystated otherwise, or apparent from context, the terms and phrases belowdo not exclude the meaning that the term or phrase has acquired in theart to which it pertains. The definitions are provided to aid indescribing particular embodiments, and are not intended to limit theclaimed invention, because the scope of the invention is limited only bythe claims. Unless otherwise defined, all technical and scientific termsused herein have the same meaning as commonly understood by one ofordinary skill in the art to which this invention belongs.

The term “nucleic acid” as used herein refers to any natural andsynthetic linear and sequential arrays of nucleotides and nucleosides,cDNA, genomic DNA, mRNA, oligonucleotides and derivatives thereof. Theterm “nucleic acid” further includes modified or derivatizednucleotides.

An “isolated” nucleic acid molecule is one which is separated from othernucleic acid molecules which are present in the natural source of thenucleic acid molecule, namely cancerous or noncancerous biologicalsamples. An “isolated” nucleic acid molecule, such as a cDNA molecule,can be substantially free of other cellular material or culture mediumwhen produced by recombinant techniques, or substantially free ofchemical precursors or other chemicals when chemically synthesized.

As used herein “oligonucleotide” refers to an oligomer or polymer ofribonucleic acid (RNA) or deoxyribonucleic acid (DNA) or mimeticsthereof. This term includes oligonucleotides composed ofnaturally-occurring nucleobases, sugars and covalent internucleoside(backbone) linkages as well as oligonucleotides havingnon-naturally-occurring portions which function similarly. Such modifiedor substituted oligonucleotides are often preferred over native formsbecause of desirable properties such as, for example, enhanced cellularuptake, enhanced affinity for nucleic acid target and increasedstability in the presence of nucleases.

As used herein an “inhibitory oligonucleotide” includes antisense,siRNA, shRNA, ribozymes and MIRs or other oligonucleotide that reducesthe expression of a targeted FOXM1 or CENPF gene or protein.

“Biological sample” refers to a sample of prostate cells. The sample canbe prostate cancer cells, for example, those taken from a prostatebiopsy from a subject having prostate cancer, or of normal prostatecells either taken from a normal control subject or in some embodimentsfrom a noncancerous area of the prostate of the subject having prostatecancer. In other embodiments, the biological sample comprisescirculating prostate cancer cells isolated from the blood, cerebrospinalfluid (CSF) or serum of a subject having prostate cancer or exosomesderived from prostate cancer cells.

“Indolent prostate cancer” means low-risk, non-aggressive ornon-invasive prostate cancers which would not lead to subject death ifleft untreated.

“Aggressive prostate cancer” means prostate cancer that leads to ashortened life expectancy of the subject or an increased occurrence ofmetastasis to other tissue cancers.

“At high risk of progressing to aggressive prostate cancer” means thatthe subject has prostate cancer that, more likely than not, is or willbecome aggressive prostate cancer.

A “subject” is a mammal, typically a human, but optionally a mammaliananimal of veterinary importance, including but not limited to horses,cattle, sheep, dogs, and cats. In some embodiments a “subject” refers toeither one who has been previously diagnosed with or identified assuffering from prostate cancer or to one who does not have prostatecancer, i.e., a normal or control subject.

As used herein, the term “diagnosis” includes the detection, typing,monitoring, dosing, and comparison at various stages of prostate cancerin a subject. Diagnosis includes the assessment of a predisposition orrisk of developing an aggressive form of prostate cancer.

As used herein, the terms “treat,” “treatment,” “treating,” or“amelioration” when used in reference to prostate cancer refer totherapeutic treatments for the prostate cancer, wherein the object is toreverse, alleviate, ameliorate, inhibit, slow down or stop theprogression of the prostate cancer to an aggressive form, or reduce theseverity of a symptom or condition. The term “treating” includesreducing or alleviating at least one adverse effect or symptom of acondition. Treatment is generally “effective” if one or more symptoms orclinical markers such as prostate-specific antigen (PSA) are reduced.

“FOXM1” as used herein refers to Forkhead box protein M1 is a proteinthat in humans is encoded by the FOXM1 gene. The protein encoded by thisgene is a member of the FOX family of transcription factors. FOXM1 isalso referred to as FKHL16; FOXM1B; HFH-11; HFH11; HNF-3; INS-1;MPHOSPH2; MPP-2; MPP2; PIG29; TGT3; TRIDENT. The human and mousereference mRNA sequences are NM_001243088 (SEQ ID NO: 49) and NM_008021(SEQ ID NO: 51), respectively. The human and mouse protein sequences areNP_001230017 (SEQ ID NO: 50) and NP_032047 (SEQ ID NO: 52),respectively. For the purpose of the methods and compositions of theinvention, “FOXM1 protein” includes orthologs (analogs in differentspecies).

“CENPF” as used herein refers to centromere protein F, a protein that inhumans is encoded by the CENPF gene. The CENPF protein associates withthe centromere-kinetochore complex. The protein is a component of thenuclear matrix during the G2 phase of interphase. CENPF is also referredto as CENF; PRO1779; hcp-1. The human and mouse reference mRNA sequencesare NM_016343 (SEQ ID NO: 53) and NM_001081363 (SEQ ID NO: 55)respectively; and the human and mouse protein sequences are NP_057427(SEQ ID NO: 54) and NP_001074832 (SEQ ID NO: 56), respectively. For thepurpose of the methods and compositions of the invention, “CENPFprotein” includes orthologs (analogs in different species).

“Protein expression” refers to expression of protein as measuredquantitatively by methods including without limitation Western blot,2-dimensional SDS-PAGE and mass spectrometry.

“mRNA expression” refers to the expression of mRNA that can be measuredquantitatively by methods including but not limited to nucleaseprotection assays, northern blots, real time quantitative PCR, andin-situ hybridization.

“Control level” and “normal level of expression” as used herein refer toa level or range of levels of FOXM1 or CENPF expressed in normalprostate tissue or indolent prostate cancer tumors.

“Threshold” or “threshold level” as used herein refers to a level orrange of levels that separate normal level of expression of FOXM1 andCENPF from a pattern, level or ranges of levels of expression of FOXM1and CENPF that indicate a high risk of aggressive prostate cancer. Whenthe levels of expression of FOXM1 and CENPF are equal to or greater thanthe threshold level then it is determined that the subject is at highrisk of developing aggressive prostate cancer or has aggressive prostatecancer, and vice versa.

“Protein” as used herein is a generic term referring to and usedinterchangeably with biologically active native protein, fragments,peptides, or analogs thereof.

“Subcellular localization of FOXM1 and CENPF” as used herein refers tothe presence of FOXM1 and CENPF inside a cell. “Colocalization” meansthat both FOXM1 and CENPF are present in the same cell or if sodesignated, in the same subcellular compartment, for examplecolocalization in the nucleus or cytoplasm.

“Master regulator” as used herein refers to a protein that acts to driveany intermediary proteins in a key signaling pathway for a phenotypetransition, such as a transition from indolent prostate cancer cell toan aggressive prostate cancer cell.

“Synergistic master regulator” as used herein refers to multiple masterregulators that together have a measured effect greater than a predictedsum of their individual measured effects.

“Cross-species computational analysis” as used herein refers toautomatically searching molecular interaction networks (“interactomes”)of two or more species, such as human and mouse models for human cells,using a computer system to discover interactions present (“conserved”)in both species.

The term “probe” refers to any molecule which is capable of selectivelybinding to a specifically intended target molecule, for example, anoligonucleotide probe that specifically hybridizes to a prognosticbiomarker mRNA such as CENPF or FOXM1, or an antibody that specificallybinds CENPF or FOXM1. Probes can be either synthesized by one skilled inthe art, or derived from appropriate biological preparations. Forpurposes of detection of the target molecule, probes may be specificallydesigned to be labeled, as described herein. Examples of molecules thatcan be utilized as probes include, but are not limited to RNA, DNA,RNA/DNA chimeras, proteins, antibodies, and organic molecules.

Unless otherwise specified, the terms “antibody” and “antibodies”broadly encompass naturally-occurring forms of antibodies (e.g., IgG,IgA, IgM, IgE) and recombinant antibodies such as single-chainantibodies, chimeric and humanized antibodies and multi-specificantibodies, as well as fragments and derivatives of all of theforegoing, which fragments and derivatives have at least an antigenicbinding site. Antibody derivatives may comprise a protein or chemicalmoiety conjugated to an antibody moiety.

DETAILED DESCRIPTION

A method, composition of matter, article of manufacture and apparatusare described for discovery of synergistic master regulators and thediagnosis of aggressive prostate cancer. In the following description,for the purposes of explanation, numerous specific details are set forthin order to provide a thorough understanding of the present invention.It will be apparent, however, to one skilled in the art that the presentinvention may be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to avoid unnecessarily obscuring the present invention.

It has been discovered that the genes encoding FOXM1 and CENPF areprognostic biomarkers that are synergistic master regulators ofaggressive prostate cancer in humans. Significantly elevatedco-expression of both FOXM1 and CENPF genes in a prostate cancer sampleat levels at least 35% above control levels is diagnostic of aggressiveprostate cancer or of a high risk of developing aggressive prostatecancer, as is described in sample embodiments. Gene expression can bedetermined by mRNA or protein expression or combinations thereof.Regulatory drivers of prostate cancer malignancy were identified byassembling genome-wide regulatory networks (interactomes) for both humanand mouse prostate cancer from expression profiling datasets of humantumors and genetically engineered mouse models, respectively.Cross-species computational analysis of these interactomes identifiedFOXM1 and CENPF as synergistic master regulators of prostate cancermalignancy that promote tumor growth by coordinated regulation of targetgene expression and activation of key signaling pathways associated withprostate cancer malignancy. Thus, co-expression of FOXM1 and CENPF wasidentified for the first time as a robust prognostic indicator ofaggressive prostate cancer with poor survival and metastasis.

Based on the data described herein, certain embodiments of the inventionare directed to methods for diagnosing aggressive prostate cancer or ahigh risk of prostate cancer progressing to an aggressive form if thelevel of mRNA or protein expression for each of FOXM1 and CENPF in aprostate cancer sample from a subject is at least 35% higher than thecorresponding level in a control prostate sample. In another embodimentaggressive prostate cancer is diagnosed if at least 50% of the cells inthe prostate cancer sample from a subject express elevated levels ofboth FOXM1 protein and CENPF protein.

1. OVERVIEW

While both FOXM1 and CENPF have been implicated in various cancers, thecurrent work has uncovered a novel synergistic interaction that had notbeen previously anticipated. FOXM1 encodes a Forkhead domaintranscription factor that is frequently over-expressed in many differenttypes of cancer, including prostate, see (Alvarez-Fernandez and Medema,2013; Halasi and Gartel, 2013a; Kalin et al., 2011; Koo et al., 2012),for reviews. Many previous studies have established a role for FOXM1expression and activity in the regulation of cellular proliferation, DNAdamage, genomic stability, drug resistance, and metastasis, and haveshown that FOXM1 interacts with other key regulators such as β-Cateninand MYB (Lefebvre et al., 2010; Zhang et al., 2011). In particular, therelevance of FOXM1 for prostate cancer has been shown by its gain- orloss-of-function in vivo, which elicit modest effects on tumor growth(Cai et al., 2013; Kalin et al., 2006).

CENPF (also known as mitosin or LEK1 in mouse), a known target of FOXM1,has also been implicated in various cancers, although not previously inprostate, and in some cases has been shown to undergo gene amplificationand be associated with disease outcome (see Ma et al., 2006; Varis etal., 2006 for reviews). However, the actual functional role of CENPF hasbeen more elusive and difficult to reconcile. In particular, while CENPFis named for its association with the centromere-kinetochore proteincomplex, such association is only transient. In fact, CENPF has beenshown to have other functions, including regulation of mitosis andcellular proliferation (Bomont et al., 2005; Feng et al., 2006; Holt etal., 2005), which are mediated in part by protein interactions,including with members of the Retinoblastoma gene family as well as withthe ATF transcription factor (see Ma et al., 2006; Varis et al., 2006for reviews).

2. CROSS SPECIES DISCOVERY OF REGULATORY GENES FOR AGGRESSIVE PROSTATECANCER

To assemble a human prostate cancer interactome, gene expression profiledata reported in (Taylor et al., 2010) was analyzed, which is ideallysuited because: (i) it is relatively large (n=185) and diverse,including gene expression profiles from primary prostate tumors,adjacent normal prostate tissue, metastases, and cell lines; (ii) itsprimary tumors encompass the full range of pathological Gleason scoresand have well-annotated clinical outcome data; and (iii) it displaysextensive genetic diversity and tumor heterogeneity, as shown byt-Distributed Stochastic Neighbor Embedding (t-SNE) analysis. Severalcharacteristics of this dataset are described below with reference toFIG. 5. Notably, interactomes assembled from three alternative humanprostate cancer datasets, also characterized in FIG. 5, were neither ascomplete nor as extensive. FIG. 1A is a block diagram and graph thatillustrate example gene profiling data for multiple human subjects withvarious stages of prostate cancer, according to an embodiment. Detailsare set forth in Example 2.

Analysis of genetically engineered mouse models (GEMMs) can circumventchallenges associated with the inherent complexity of the moreheterogeneous human cancer phenotypes. Investigations of mouse models ofprostate cancer have contributed to characterization of disease-specificpathways, led to the identification of biomarkers of diseaseprogression, and provided useful preclinical models for prevention andtherapy (Irshad and Abate-Shen, 2013; Ittmann et al., 2013). Followingthe description of the first transgenic model of prostate cancer nearly20 years ago, there are now numerous GEMMs that collectively model keymolecular pathways de-regulated in human prostate cancer, andrecapitulate the various stages of disease progression includingpre-invasive lesions (prostate intraepithelial neoplasia, PIN),adenocarcinoma, castration-resistance, and metastasis (Irshad andAbate-Shen, 2013; Ittmann et al., 2013).

Inherent species differences often hinder direct comparative analyses ofmouse models and human cancer. As described herein, a novel combinationof computational approaches were applied to enable accuratecross-species integration of regulatory information from mouse to man inthe context of prostate cancer. Recent advances in systems biology haveled to the reverse engineering of regulatory networks (interactomes)that integrate large-scale datasets encompassing gene expressionprofiles, protein-protein interactions, genomic alterations, andepigenetic changes associated with cancer and other diseases (seeLefebvre et al., 2012 for a review). While individual analyses of humanand murine interactomes led to relevant biological discoveries,cross-species interactome-based interrogation strategies have not beensystematically implemented until now.

The results described here are based on an approach for accuratecross-species analysis of conserved cancer pathways based on reverseengineering and interrogation of genome-wide regulatory networks (i.e.,interactomes) representing both human and mouse prostate cancer. Toaccomplish this, the first regulatory network obtained from in vivoperturbation of a repertoire of mouse cancer models was introduced, aswell as its comparative analysis with a complementary regulatory networkgenerated from human prostate cancer samples. Cross-speciescomputational interrogation of these paired interactomes followed byexperimental validation thus elucidated the synergistic interaction ofFOXM1 and CENPF as a driver of aggressive prostate cancer malignancy.

To assemble a corresponding mouse prostate cancer interactome, it wasfirst necessary to generate an appropriately sized gene expressionprofile dataset representing sufficient expression variability. Toaddress this challenge, 13 distinct GEMMs were first selected, whichtogether represent the full spectrum of prostate cancer phenotypes,including normal epithelium (wild-type), low-grade PIN (Nkx3.1 and APT),high-grade PIN and adenocarcinoma (APT-P, APC, Hi-Myc, NP, Erg-R, andNP53), castration-resistant prostate cancer (NP-AI), and metastaticprostate cancer (NPB, NPK, and TRAMP). FIG. 1B is a block diagram andgraph that illustrate example gene profiling data for multiple mousemodels for prostate cancer, according to an embodiment. The diagramgroups the mouse models by phenotype listed above. The graph plots thet-SNE analysis showing relative distribution of the GEMMs. More detailis set forth in Example 2.

To generate a sufficient number of samples, while further increasing thevariability of the corresponding expression profiles, a controlled setof exogenous perturbations was introduced by in vivo administration of13 different small-molecule perturbagens to each GEMM. Perturbagens wereselected for their clinical relevance and/or ability to modulate keyprostate cancer pathways, including: hormone signaling (testosterone,calcitriol, or enzalutamide); PI3 kinase activity (MK2206, LY294002, andrapamycin); MAP kinase activity (PD035901); tyrosine kinase activity(imatinib, dasatinib, and sorafenib); NF1B signaling (BAY 11-7082);JAK/STAT activity (WP1066); and chemotherapy (docetaxel). Followingpilot studies to define the appropriate dose and schedule that producedthe broadest range of gene expression changes, a universal schedule wasadopted of 1 treatment per day for 5 days with dosage determinedindependently for each perturbagen, as described below in anexperimental procedures section.

The resulting dataset contained 384 gene expression profiles,corresponding to the 13 GEMMs each treated with the 13 perturbagens orvehicles. The t-SNE analysis revealed that the resulting mouse datasetrepresented an extensive range of gene expression variability, asrequisite for ARACNe. Specifically, while expression profiles from thesame GEMMs and perturbagens clustered together, suggesting their effectwas highly replicable, the diverse GEMMs and perturbagens providedindependent and highly effective axes of expression heterogeneity. FIG.1C is a block diagram and graph that illustrate example effects on geneprofiling data for mouse models in response to various perturbagens,according to an embodiment. The schematic diagram depicts perturbagensused to treat the GEMMs. The graph plots the t-SNE analysis showing therelative distribution of perturbagens for a representative GEMM (i.e.,the NP model).

Regulatory networks (interactomes) for human and mouse prostate cancerwere generated using the Algorithm for the Reconstruction of AccurateCellular Networks. FIG. 2A is a block diagram and graph that illustratesexample interactomes for human and mouse models with prostate cancer,according to an embodiment. The suitability of these mouse and humaninteractomes for cross-species interrogation was next evaluated bydeveloping a novel computational approach to assess the globalconservation of their transcriptional programs described in detail inExample 2. Notably, conserved transcriptional regulators included manygenes known to play important roles in prostate cancer, such as AR,ETS1, ETV4, ETV5, STAT3, MYC, BRCA1, and NKX3.1. In particular, ARdisplayed extensive correlation of its transcriptional activity betweenthe human and mouse interactomes, consistent with its known role as akey regulator of prostate development and prostate tumorigenesis

The Master Regulator Inference algorithm (MARINa) (Carro et al., 2010;Lefebvre et al., 2010) was used to infer candidate master regulators(MRs) that act individually or synergistically to drive malignantprostate cancer in the conserved interactomes. MARINa estimatesdifferential activity (DA) based on enrichment (differential expression,DE) of their activated and repressed targets in the malignancysignature. More specifically, MARINa identified candidate MRs based onthe concerted differential expression of their ARACNe-inferred targets(i.e., their differential activity, DA). Specifically, “activated” MRshave positively-regulated and repressed targets significantly enrichedamong upregulated and downregulated genes, respectively, while“repressed” MRs have the converse. To interrogate the human prostatecancer interactome, a gene signature was used representing prostatecancer malignancy from the Taylor dataset, which compares aggressiveprostate tumors (Gleason score ≧8 with rapid biochemical recurrence;sample size n=10) versus indolent ones (Gleason score 6 tumors with nobiochemical recurrence; sample size n=39). The resulting independentlists of human and mouse MRs were then integrated to produce a rankedlist of 20 conserved MRs, including 7 activated and 13 repressed (jointp-value: p≦0.0074 by Stouffer's method). Notably, these conserved MRswere more likely to be associated with disease outcome than thenon-conserved ones, and were also more likely to be differentiallyexpressed in aggressive prostate tumors (metastatic versusnon-metastatic; 100% versus 60%). FIG. 3C is a table that illustratesexample ranking of master regulators for their impact on prostate cancerby various available algorithms. Using the ARACNe method to analyze allpossible pairs among the conserved activated MRs, the only pair that wasfound to be statistically significant was FOXM1 and CENPF. Both FOXM1and CENPF were differentially co-expressed at significantly elevatedlevels in aggressive prostate tumors and were predicted to besignificantly associated with disease outcome. Thus, subsequent analyseswere focused on this pair of cross-species conserved, synergistic MRs.

3. METHOD OF DIAGNOSIS FOXM1 and CENPF are Prognostic Biomarkers ofAggressive Prostate Cancer

Analysis of high-density tissue microarrays (TMAs) revealed that theco-expression of FOXM1 and CENPF constituted a highly informativebiomarker of poor disease outcome. FIG. 5 is a table that illustratesexample clinical datasets used to determine whether synergistic masterregulators FOXM1 and CENPF are prognostic biomarkers of prostate canceroutcomes, according to an embodiment. The datasets are listed along thetop row, with their use in this study grouped by primary dataset (Tayloret al., 2010); secondary datasets; RNA gene expression datasets (Sboneret al., 2010; Glinsky et al., 2004); and protein immunohistochemistrytissue microarray (TMA) datasets (Outcome TMA from MSKCC, and MetastasisTMA from Michigan). The categories of data in each dataset are given bythe rows, as applicable. One row breaks down the number of samples foreach cell type; one row gives the median age of the subjects. The nextrows give the Pathology T stage; the clinical T stage; the Pathology Nstage; the Pathology Gleason score; the biopsy Gleason score; thesurvival index (SVI); the extracapsular extension percentage; thebiochemical recurrence (BCR) median time in months; the median overallsurvival in months; and the median time to metastasis in months.

Analysis of protein expression of FOXM1 and CENPF was performed usinghigh-density tissue primary tumor microarray (TMAs) (Donovan et al.,2008) and a metastasis TMA (Shah et al., 2004). Availableclinico-pathological features of these cohorts as well as independenthuman datasets used for clinical validation are summarized in the Tableof FIG. 5.

In particular, a high-density TMA containing primary tumors from a largecohort of subjects (sample size n=916) that had undergone prostatectomyat Memorial Sloan-Kettering Cancer Center from 1985 to 2003 (Donovan etal., 2008) was analyzed. These cases have extensive clinical follow-updata for up to 20 years, including time to biochemical recurrence,prostate-cancer specific survival, and time to metastasis. A second TMAwas evaluated from the rapid autopsy program at the University ofMichigan containing prostate cancer metastases (sample size n=60),including 6 lung, 11 liver, 22 lymph node, and 14 other sites (Shah etal., 2004). Immunostaining for FOXM1 or CENPF was performed on adjacentsections of each TMA slide and staining intensity was evaluated (seeexperimental procedures section, below).

FIG. 6A is an image that illustrates example micrographs of FOXM1 andCENPF stained tissues showing enhanced concentrations of both inaggressive prostate cancer tumors compared to other prostate tumors,according to an embodiment. These micrographs are based on the MSKCCprostatectomy TMA; and, analysis revealed that FOXM1 and CENPF wereover-expressed in 33% and 37% of all cases, respectively (sample sizen=821 informative cases), with a trend toward increased expression intumors with higher Gleason scores. Spearman rank correlation coefficientof FOXM1 and CENPF protein expression levels was 0.57 with p value<2.2×10⁻¹⁶, indicating the coexpression relationship is highlysignificant.

FIG. 6B is an image that illustrates example micrographs of FOXM1- andCENPF-stained tissues showing enhanced concentrations of both inprostate cancer that metastasized to lung and liver tumors. Thesemicrographs are based on the Michigan metastasis TMA; and, analysisrevealed that FOXM1 and CENPF were coexpressed in most of the prostatecancer metastases (88% and 90%, respectively, sample size n=53informative cases) at significantly elevated levels. Spearman rankcorrelation coefficient of FOXM1 and CENPF protein expression levels was0.43 with p value <0.001, indicating the coexpression is significant.

Thus, co-expression of FOXM1 and CENPF at above-threshold levels,particularly their nuclear colocalization, as described in more detailbelow, was well correlated in both the MSKCC prostatectomy TMA and theMichigan metastasis TMA. Additionally, both FOXM1 and CENPF wereoverexpressed at the mRNA level and their co-expression waswell-correlated in advanced prostate cancer and metastases fromindependent cohorts of human prostate cancer.

To determine whether expression of FOXM1 and/or CENPF is associated withdisease outcome on the MSKCC TMA, 4 groups of subjects were definedbased on their expression levels: (i) low/normal expression of bothFOXM1 and CENPF (sample size n=418); (ii) high expression of FOXM1 andlow/normal expression of CENPF (sample size n=97); (iii) high expressionof CENPF and low/normal expression of FOXM1 (sample size n=133); and(iv) high expression of both FOXM1 and CENPF (sample size n=173). FIG.6C through FIG. 6E are graphs that illustrate example Kaplan-Meiersurvival analysis based on protein expression levels of FOXM1 and CENPFwith respect to time to biochemical recurrence, time to prostatecancer-specific death, or time to metastatic progression, respectively,according to an embodiment.

Kaplan-Meier survival analysis of these subject groups revealed thatthose having elevated expression of both FOXM1 and CENPF were associatedwith the worst outcome with high significance (low values of p) forthree independent clinical endpoints, namely, time to biochemical-freerecurrence (p≦4.4×10⁶), death due to prostate cancer (p≦5.9×10⁻⁹), andtime to metastasis (p≦1.0×10⁻¹⁶). The p-values correspond to a log-ranktest and indicate the statistical significance of the association withoutcome for each indicated branch compared to control (i.e., subjectswith low protein expression of both FOXM1 and CENPF). Notably,co-subcellular localization of FOXM1 and CENPF in prostate tumors wasalso associated with the worst outcome for all three independentclinical endpoints, as described in more detail below. In contrast,elevated expression of only FOXM1 or CENPF was either not significant ormarginally significant for biochemical recurrence and prostate-specificsurvival (p≦0.053 and p≦0.011 for FOXM1, respectively; p≦0.078 andp≦0.402 for CENPF, respectively), and was 10 to 13 orders of magnitudeless significant, respectively, than co-expression for time tometastasis (p≦0.001 for FOXM1 and p≦3.1×10⁻⁶ for CENPF, respectively).

Association of FOXM1 and CENPF with disease outcome was independentlycorroborated in two independent human prostate cancer datasets that hadnot been used for training purposes elsewhere in this study; namely, theGlinsky dataset, in which biochemical recurrence is the clinicalendpoint (Glinsky et al., 2004), and the Sboner dataset, in which theclinical endpoint is prostate cancer-specific overall survival (Sboneret al., 2010). Using these independent cohorts, the mRNA expressionlevels of FOXM1 and CENPF was evaluated as well as their MARINa-inferredactivity. Kaplan-Meier survival analysis was then performed on 4 subjectgroups: (i) those with low inferred activity or expression for FOXM1 andCENPF; (ii) those with high inferred activity or expression only forFOXM1; (iii) those with high inferred activity or expression only forCENPF; and (iv) those with high inferred activity or expression for bothFOXM1 and CENPF. FIG. 6F and FIG. 6G are graphs that illustrate exampleKaplan-Meier survival analysis based on the interactome-inferredactivity levels of FOXM1 and CENPF with respect to time to biochemicalrecurrence, or time to prostate cancer-specific death, respectively,according to an embodiment.

Similar to the analysis of protein expression on the TMA, subjects withhigh inferred activity or mRNA expression for both CENPF and FOXM1 wereassociated with the worst outcome in both cohorts, as measured bybiochemical recurrence (p≦6.5×10⁻⁵) and prostate cancer-specificsurvival (p≦4.0×10⁻⁵). The ARACNe-inferred activities levels wereassessed for each subject sample in both cohorts. The p-valuescorrespond to a log-rank test and indicate the statistical significanceof the association with outcome for each indicated branch compared tocontrol (i.e., subjects with low activity levels of both FOXM1 andCENPF). Notably, these findings reveal that their ARACNe-inferredactivities are well-correlated with the actual expression of FOXM1 andCENPF proteins on the TMA, and further demonstrate the strikingassociation of their co-expression/co-activity with poor diseaseoutcome.

FIG. 7 is a table that illustrates example prognostic power ofco-expression of protein levels of FOXM1 and CENPF, with death due toprostate cancer and time to metastasis as evaluation endpoints,according to an embodiment. C-statistics give the proportion of pairs inwhich the predicted event probability (e.g., probability of survivalfrom prostate cancer) is higher for the subject who experienced theevent of interest (e.g., coexpression of FOXM1 and CENPF) than that ofthe subject who did not experience the event. Analysis of co-expressionof FOXM1 and CENPF on the MSKCC prostatectomy TMA using C-statisticsrevealed their robust prognostic value for disease-specific survival(C=0.71; confidence interval=0.59-0.84, p≦2.4×10⁻⁴), as well as time tometastasis (C=0.77; confidence interval=0.71-0.83, p≦3.0×10⁻¹⁹).Notably, co-expression of FOXM1 and CENPF proteins as diagnostic markersof aggressive prostate cancer dramatically improved the prognostic valuecompared to Gleason score alone, for both disease-specific survival(C=0.86; confidence interval=. 0.80-0.93, p≦1.0×10⁻³⁰; p value forimprovement, p≦2.0×10⁻⁴) and time to metastasis (C=0.86; confidenceinterval=0.81-0.89, p≦6.5×10⁻⁵⁸; p value for improvement, p≦5.3×10⁻¹³).In certain embodiments of the invention, diagnosis of aggressiveprostate cancer further includes determining, in addition to elevatedcoexpression of both FOXM1 and CENPF, high Gleason scores of score ≧8.

Taken together, these analyses of independent clinical cohorts usingdistinct statistical models demonstrate that elevated levels ofco-expression of FOXM1 and CENPF is a good predictor of disease outcome.In an embodiment FOXM1 and CENPF are prognostic for aggressive prostatecancer or of prostate cancer progressing to an aggressive form when theyare coexpressed at elevated levels of at least 35% compared to thelevels expressed in control prostate tissue. Based on the results,certain embodiments are directed to a method for diagnosing aggressiveprostate cancer or of identifying subjects with prostate cancer that isat high risk of progressing to an aggressive form by a) obtaining a testprostate cancer sample from a subject having prostate cancer, and acontrol prostate tissue sample,

b) determining a level of expression of the prognostic genes (FOXM1)Forkhead box protein M1 and Centromere, protein F (CENPF) in the testand control samples, c) comparing the level of expression of prognosticgenes FOXM1 and CENPF in the test sample to the corresponding level inthe control sample, and d) if the level of expression of both of theprognostic genes FOXM1 and CENPF in the test sample is at least 35%higher than the corresponding level in the control sample, thendetermining that the subject has an aggressive form of prostate canceror is at high risk of developing an aggressive form of prostate cancer.

In certain embodiments the level of expression of FOXM1 and CENPF isdetermined by the level of mRNA encoding FOXM1 and CENPF, respectively;or by the level of FOXM1 protein and CENPF protein in the sample orcombinations thereof. In another embodiment, a diagnosis of aggressiveprostate cancer is reached by a) obtaining a prostate cancer sample froma subject having prostate cancer, b) determining a level of expressionof FOXM1 protein and CENPF protein expression in the cancer cells in thesample by immunostaining with a first antibody that specifically bindsto FOXM1 and a second antibody that specifically binds to CENPF, and c)diagnosing aggressive prostate cancer if at least 50% of the cells inthe test sample express both FOXM1 protein and CENPF protein at acomposite score of at least 100 for each protein, wherein the compositescore is calculated by multiplying the percent staining value by thestaining intensity value. Any other method for determining that at least50% of the cells in a prostate cancer sample coexpress both FOXM1 andCENPF proteins at levels of at least 35% above levels in controlprostate tissue can be used, these include flow cytometry withdifferential florescent labeling of both proteins.

FIG. 8 is a flow chart that illustrates an example diagnostic method 800for determining whether a subject has or is at high risk of undergoing aspecific phenotypic transition based on coexpression of at least twosynergistic master regulators, such as FOXM1 and CENPF for aggressiveprostate cancer, according to an embodiment. Although steps are depictedin FIG. 8, and in subsequent flowchart FIG. 10, as integral steps in aparticular order for purposes of illustration, in other embodiments, oneor more steps, or portions thereof, are performed in a different order,or overlapping in time, in series or in parallel, or are omitted, or oneor more additional steps are added, or the method is changed in somecombination of ways.

In step 810, a subject is identified who has or is at high risk ofproducing the phenotype transition of interest, here development ofaggressive prostate cancer from a prostate tumor or nodule detected in asubject. In step 803 a sample is taken from the identified subject, suchas a biopsy of the prostate tumor or nodule. Biological samples for usein the present embodiments also include circulating prostate cancercells or prostate tumor cells which can be detected using a variety ofmethods known in the art that select cells based on surface markers forexample using antibodies against the surface markers, or expression ofother prostate cancer markers. In some embodiments the prostate cancercells can be selected by size. Biological samples for use in the presentembodiments also include exosomes derived from prostate cancer cells,which are cell-derived vesicles that are present in many and perhaps allbiological fluids, including blood. The reported diameter of exosomes isbetween 30 and 100 nm.

In step 805, the pattern of coexpression of synergistic masterregulators, such as FOXM1 and CENPF, for the phenotype transition, suchas to aggressive prostate cancer, is determined. For example, thepattern of certain genes' expression is determined by determining therelative level of coexpression of mRNA coding for the master regulators,or the intensity of immunostaining of polypeptides encoded by the masterregulators. Step 805 is described in more detail below with reference toFIG. 10.

In step 807, it is determined whether the synergistic master regulatorsare coexpressed at significantly elevated (or reduced) levels above(below) some threshold level, or otherwise have different patterns than,determined in control prostate tissue. For diagnosis of aggressiveprostate cancer or the risk of a tumor progressing to an aggressiveform, it is determined whether FOXM1 and CENPF levels are both abovecorresponding threshold control levels, which are the levels seen innormal prostate tissue or indolent prostate cancer tumors. The thresholdor pattern depends on the measurement type as described in more detailbelow with reference to FIG. 10.

If it is determined in step 807, that the synergistic master regulatorsare coexpressed at some elevated (or reduced) level or other differentpattern, then control passes to step 811. Otherwise control passes tostep 821.

In step 811, it is determined that the subject is at risk for developingthe phenotype transition, or in fact is undergoing, or has undergone,the phenotype transition. In some embodiments the phenotype transitionis to aggressive prostate cancer. Control then passes to step 817 totreat the subject based on this risk or diagnosis.

In step 821, it is determined that the subject does not have or is atlow risk or no risk for developing the phenotype transition, e.g.,aggressive prostate cancer; and, the process ends, or is repeated withthe same subject at a later time or with another subject.

More information on the secondary effects of elevated co-expression ofFOXM1 and CENPF was obtained by experiments that were performed tovalidate synergistic interactions of master regulators and elucidateunderlying mechanisms, as well as to evaluate their relevance forclinical outcome. FIG. 9A is a graph shows relative mRNA expressionlevels for the indicated genes in the indicated cell lines followingindividual or co-silencing (i.e., silencing both) of FOXM1 and CENPF.The p-values (indicated by one or two *) show the significance of thepredicted additive effect versus actual observed effect on geneexpression (*=p<0.01; **=p<0.001). Silencing was performed usinglentivirus vectors for shRNA for each or both of the two genes FOXM1 andCENPF, as described in more detail below. The ARACNe-inferred commontarget genes include BRCA1, BUB1, KI67, CYCLIN A, TIMELESS, CDC25,TRIP13, PLK1, HHMR, MYBL2, BIRC5, AURKA, AURKB.

Although target gene expression was somewhat reduced by their individualsilencing, as shown in FIG. 9A, co-silencing of FOXM1 and CENPF produceda significantly greater reduction for the majority of targets,consistent with the synergistic regulation of target gene expression byFOXM1 and CENPF. Notably, these findings were observed in each cell linethat express both FOXM1 and CENPF, but not in LNCaP cells which do notexpress CENPF.

In addition, analyses of genomic binding of FOXM1 to its known targetsites using chromatin immunoprecipitation (ChIP) was followed byquantitative PCR analyses. FIG. 9B is a graph that illustrates exampleenrichment of FOXM1 binding normalized to input with and withoutsilencing of CENPF, according to an embodiment. Cells were infected witha lentivirus expressing a V5-tagged FOXM1 plus shRNA CENPF (or acontrol) and ChIP was done using an anti-V5 antibody. Data are expressedas fold of enrichment of FOXM1 binding normalized to input. Thisrevealed that FOXM1 binding to its targets was abrogated by silencingCENPF, therefore suggesting that CENPF is required for appropriategenomic binding by FOXM1.

Interestingly, although a direct protein-protein interaction of FOXM1and CENPF in co-immunoprecipitation assays was not observed, it wasobserved that FOXM1 and CENPF were co-localized in the nucleus ofprostate cancer cells and that their subcellular colocalization wasmutually dependent. FIG. 9C is an image of micrographs that illustrateexample changes in subcellular localization of FOXM1 and CENPF proteinsin prostate cancer cells after silencing either, according to anembodiment. Shown are microphotographs of immunofluorescence stainingfor FOXM1 or CENPF in the control or silenced cells as indicated. Arrowsindicate subcellular localization or the shift in localization followingsilencing.

In particular, silencing of CENPF resulted in the redistribution ofFOXM1 to the cytoplasm as well the nucleus, and conversely silencing ofFOXM1 resulted in the accumulation of CENPF at the nuclear periphery.Notably, subcellular co-localization of FOXM1 and CENPF was alsoobserved in human prostate tumors and associated with disease outcome.Taken together, these findings show that FOXM1 and CENPF synergisticallyregulate expression of mutual target genes, which mediated in partthrough their subcellular colocalization in prostate cancer cells.

Determining Co-Expression of the Synergistic Master Regulators FOXM1 andCENPF

FIG. 10 is a flow chart that illustrates an example method 1000 fordetermining coexpression of the synergistic master regulators such asFOXM1 and CENPF, according to an embodiment. Method 1000 is a particularembodiment of step 805, described above. In step 1051 it is determinedwhether to use expression of the genes for the synergistic masterregulators, or expression of the master regulator proteins (e.g.,polypeptides included), or expression of the genes or polypeptides ofone or more the targets in the signaling pathways of the masterregulators, or combinations thereof.

In step 1053, it is determined whether the gene expression orpolypeptide expression is to be evaluated. If the gene expression is tobe determined, control passes to step 1061. Otherwise control passes tostep 1071.

In step 1061, the normalized level of mRNA is determined for FOXM1 andCENPF in the prostate sample. Certain methods and primers fordetermining the relative or normalized levels of mRNA for FOXM1 andCENPF compared to other genes are also described in the experimentalprocedures section, below, with primer sequences listed in Table 1 inthat later section. In an example, total RNA was isolated from a subjectsample of prostate tissues/tumors using a MagMAX-96 total RNA isolationkit and biotin-labeled using the Illumina TotalPrep RNA AmplificationKit (Life Technologies). Slides were scanned using an iScan (Illumina)and the resulting files were uploaded and background-corrected inBeadStudio 3.1.3.0 (Illumina, Inc.). Expression profiling data werenormalized using standard variance stabilizing transformation (VST) androbust spline normalization (RSN) with lumiT and lumiN functions fromlumi library, in R-system v2.14.0 (The R Foundation for StatisticalComputing, ISBN 3-900051-07-0).

In step 1063, the threshold for significantly elevated coexpression isdetermined, e.g., retrieved from data storage. An mRNA level in thesubject prostate cancer sample that is at least 35% higher than thelevel expressed in normal prostate tissue for each gene is consideredelevated. In other embodiments other thresholds are used. In someembodiments, the threshold for each to be considered elevated isselected in a range from about 35% to about 100% or more. In someembodiments the threshold is at least 50%; and in other embodiments thethreshold is at least 75%. Control then passes to step 807 of FIG. 8,described above, to determine if the measured level exceeds thethreshold.

In step 1071, the level of immunostaining is determined for FOXM1 andCENPF proteins. Certain antibodies for immunostaining FOXM1 and CENPFare identified in the experimental procedures section below and listedin Table 2, and procedures for quantifying the intensity levels are alsodescribed. Any antibody that selectively binds to either FOXM1 or CENPFcan be used. Protein levels were determined by percent of staining(e.g., from 0 to 100%) and intensity level of staining (e.g., 0, 1, 2,or 3) in each tumor sample. A composite protein level is determined bymultiplying percent of staining and its intensity level for each tumorsample, for FOXM1 or CENPF. In some embodiments, step 1071 includesdetermining the relative amounts of FOXM1 and CENPF inside the membraneof the nucleus of the cells where the staining is observed in the toprow of FIG. 9C when both FOXM1 and CENPF are expressed. In variousembodiments, this determination of the relative amount within thenucleus is done in addition to, or instead of, determining the compositeprotein level.

In step 1073, the threshold for elevated coexpression is determined,e.g., retrieved from data storage. Composite protein level exceeding 100for each protein was considered elevated. Thus, in some embodiments,FOXM1 and CENPF are considered to be co-expressed if the determinedcomposite protein level in the subject prostate tumor sample for each isabove about 100.

In some embodiments, the pattern of coexpression is determined by therelative amount of the total FOXM1 and CENPF that is inside the samecell, or in a particular subcellular compartment, such as inside of thenuclear membrane. For example, if at least 50% of the cells in the testsample express both FOXM1 protein and CENPF protein at a composite scoreof at least 100 for each protein, then determining that coexpression iselevated.

Control then passes to step 807 of FIG. 8, described above, to determineif the measured level exceeds the threshold.

Method for Detecting mRNA Expression

In some embodiments, the methods described herein comprise detecting thepresence of FOXM1 or CENPF RNA expression (e.g., mRNA expression),including detecting of the absolute or relative quantity of the RNA, thehalf-life of the RNA, a splicing or processing of the RNA, the nuclearexport of the RNA or the sub-cellular location of the RNA. Suchdetection can be by various techniques known in the art, including bysequencing all or part of the FOXM1 or CENPF RNA or by selectivehybridization or selective amplification of all or part of the FOXM1 orCENPF RNA. As described herein, there exist many suitable methods fordetecting the presence and level of a nucleic acid encoding a FOXM1polypeptide or a CENPF polypeptide, including, but not limited togenotyping a sample, for example via gene sequencing, selectivehybridization, amplification, gene expression analysis (e.g. microarrayanalysis), oligonucleotide ligation assay, a confirmation based assay, ahybridization assay, a sequencing assay, an allele-specificamplification assay, a microsequencing assay, a melting curve analysis,a denaturing high performance liquid chromatography (DHPLC) assay (forexample, see Jones et al., 2000), or a combination thereof. Sequencingcan be carried out using techniques well known in the art, usingautomatic sequencers. The sequencing can be performed on the completegene or on specific domains thereof, such as those known or suspected tocarry deleterious mutations or other alterations. Other suitable methodsinclude allele-specific oligonucleotide (ASO), oligonucleotide ligation,allele-specific amplification, Southern blot (for DNAs), Northern blot(for RNAs), single-stranded conformation analysis (SSCA), PFGE,fluorescent in situ hybridization (FISH), gel migration, clampeddenaturing gel electrophoresis, denaturing HLPC, melting curve analysis,heteroduplex analysis, RNase protection, chemical or enzymatic mismatchcleavage, ELISA, radio-immunoassays (RIA) and immuno-enzymatic assays(IEMA). Some other approaches are based on specific hybridizationbetween nucleic acids from the subject and a probe specific for wildtype gene or RNA. The probe can be in suspension or immobilized on asubstrate. The probe can be labeled to facilitate detection of hybrids.Some of these approaches are suited for assessing a polypeptide sequenceor expression level, such as Northern blot, ELISA and RIA. These latterrequire the use of a ligand-specific for the polypeptide, for example,the use of a specific antibody.

In certain embodiments, detection or quantification of a nucleic acidencoding a nucleic acid encoding a FOXM1 polypeptide or a CENPFpolypeptide (or a fragment thereof) can be by hybridization basedmethods. In certain embodiments, hybridization-based detection methodscan employ a step of forming specific hybrids between complementarynucleic acid sequences that serve to detect nucleic acid sequences.Microarrays are a suitable hybridization based detection technique thatcan be used in connection with the methods described herein. Microarraysemploy nucleic acid probes specific for wild type gene or RNA and can beused to investigate the expression of a nucleic acid encoding a FOXM1polypeptide or a CENPF polypeptide in samples from patients in adiagnostic context. In general, microarrays comprise a two dimensionalarrangement of nucleic acid or polypeptide probes which comprises anintentionally created collection of nucleic acid or polypeptide probesof any length spotted onto a substrate/solid support. The array itselfcan have different formats, e.g. libraries of soluble probes orlibraries of probes tethered to resin beads, silica chips, or othersolid supports. The process of microarray fabrication is well-known tothe person skilled in the art. The process can comprise preparing aglass (or other) slide (e.g., chemical treatment of the glass to enhancebinding of the nucleic acid probes to the glass surface), obtaining DNAsequences representing genes of a genome of interest, and spottingsequences these sequences of interest onto glass slide. Sequences ofinterest can be obtained via creating a cDNA library from an mRNA sourceor by using publicly available databases, such as GeneBank, to annotatethe sequence information of custom cDNA libraries or to identify cDNAclones from previously prepared libraries. Generally, it isrecommendable to amplify obtained sequences by PCR in order to havesufficient amounts of DNA to print on the array. The liquid containingthe amplified probes can be deposited on the array by using a set ofmicrospotting pins. Ideally, the amount deposited should be uniform. Theprocess can further include UV-crosslinking in order to enhanceimmobilization of the probes on the array. Microarray chips suitable foruse with the methods described herein are well known to those of skillin the art (see, e.g., U.S. Pat. Nos. 6,308,170; 6,183,698; 6,306,643;6,297,018; 6,287,850; 6,291,183, each incorporated herein by reference).These are exemplary patents that disclose nucleic acid microarrays andthose of skill in the art are aware of numerous other methods andcompositions for producing microarrays. A microarray composition of thepresent invention can be employed for the diagnosis and treatment of anycondition or disease in which the expression of FOXM1 and/or CENPF isimplicated. The microarray-based methods can be used for large scalegenetic or gene expression analysis of a large number of targetsequences, including nucleic acids encoding a FOXM1 polypeptide ornucleic acids encoding a CENPF polypeptide. The microarray can also beused in the diagnosis of diseases and in the monitoring of treatments.Further, microarrays can also be employed to investigate an individual'spredisposition to a disease. Furthermore, the microarrays can beemployed to investigate cellular responses to infection, drug treatment,and the like.

When microarrays are used in connection with the methods describedherein, the formation of a plurality of detectable complexes betweenprobes and target nucleic acid sequences can be assessed. The expressionprofiles can show unique expression patterns that are characteristic ofthe presence or absence of a disease or condition, such as a malignantprostate cancer. In certain embodiments where expression profiles areexamined using microarray technology, complexes can be formed byhybridization of one or more probes having complementarity to a nucleicacid encoding a FOXM1 polypeptide or a nucleic acid encoding a CENPFpolypeptide. Such a microarray can be employed in several applicationsincluding diagnostics, prognostics and treatment regimens, drugdiscovery and development, toxicological and carcinogenicity studies,forensics, pharmacogenomics, and the like. The probe can be insuspension or immobilized on a substrate or support (for example, as innucleic acid array or chips technologies). For example, a sample fromthe subject can be contacted with a nucleic acid probe specific for anucleic acid encoding a FOXM1 polypeptide or a nucleic acid encodingCENPF polypeptide.

In certain embodiments, the expression profile can be used to a nucleicacid encoding a FOXM1 polypeptide or a nucleic acid encoding CENPFpolypeptide infer changes in the expression of target genes implicatedin disease wherein the expression of such target genes is upregulated ordownregulated by FOXM1, CENPF, or by the concerted action of FOXM1 andCENPF. Example probes and primers useful for obtaining gene expressionprofiles in normal and malignant cells, and comparing the geneexpression in malignant and corresponding normal cells are known in theart (Okabe et al., 2001; Kitahara et al., 2001; Lin et al., 2002;Hasegawa et al., 2002).

In certain embodiments, microarray-based detection and/or quantificationof a nucleic acid encoding FOXM1 polypeptide and/or a nucleic acidencoding or a CENPF polypeptide can comprise steps of providing abiological sample from a person suspected of having a cancer (e.g. amalignant prostate cancer), and determining the level of expression of anucleic acid encoding FOXM1 polypeptide and/or a nucleic acid encodingor a CENPF polypeptide in the cells of the biological sample. Inparticular, such embodiments of the methods described herein cancomprises comprising the following steps: (a) contacting a cell samplenucleic acid with a microarray under conditions suitable forhybridization; (b) providing hybridization conditions suitable forhybrid formation between the cell sample nucleic acid and apolynucleotide of the microarray; (c) detecting the hybridization; and(d) diagnosing the disorder condition based on the results of detectingthe hybridization.

For example, methods of purification of nucleic acids are described inTijssen Laboratory Techniques in Biochemistry and Molecular Biology:Hybridization With Nucleic Acid Probes, Part I. Theory and Nucleic AcidPreparation, Elsevier, New York, 1993. In one case, total RNA isisolated using the TRIZOL reagent (Life Technologies, Gaithersburg Md.),and mRNA is isolated using oligo d (T) column chromatography or glassbeads. Alternatively, when target polynucleotides are derived from anmRNA, the target polynucleotides can be a cDNA reverse transcribed froman mRNA, an RNA transcribed from that cDNA, a DNA amplified from thatcDNA, an RNA transcribed from the amplified DNA, and the like. When thetarget polynucleotide is derived from DNA, the target polynucleotide canbe DNA amplified from DNA or RNA reverse transcribed from DNA. In yetanother alternative, the targets are target polynucleotides prepared bymore than one method.

When target polynucleotides are amplified, it is desirable to amplifythe nucleic acid sample and maintain the relative abundances of theoriginal sample, including low abundance transcripts. Total mRNA can beamplified by reverse transcription using a reverse transcriptase and aprimer consisting of oligo d(T) and a sequence encoding the phage T7promoter to provide a single stranded DNA template. The second DNAstrand is polymerized using a DNA polymerase and a RNAse which assistsin breaking up the DNA/RNA hybrid. After synthesis of the doublestranded DNA, T7 RNA polymerase can be added, and RNA transcribed fromthe second DNA strand template (Van Gelder et al. U.S. Pat. No.5,545,522). RNA can be amplified in vitro, in situ or in vivo (seeEberwine, U.S. Pat. No. 5,514,545).

The sequence of the probes and primers suitable for use withhybridization or amplification based detection methods described hereincan be derived from the sequences of a nucleic acid encoding a FOXM1polypeptide or a CENPF polypeptide. According to the invention, a probecan be a polynucleotide sequence which is complementary to andspecifically hybridizes with a, or a target portion of a nucleic acidencoding a FOXM1 polypeptide or a CENPF polypeptide, such as a DNA orRNA molecule encoding such polypeptides. Probes and primers suitable foruse with the methods described herein include those that arecomplementary to a nucleic acid encoding a FOXM1 polypeptide or a CENPFpolypeptide, can comprise single-stranded nucleic acids of between 8 to1000 nucleotides in length, for instance between 10 and 800, between 15and 700, or between 20 and 500. Exemplary probes and primers may be 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95, or more 100 nucleotides in length.In one embodiment, a useful probe or primers of the invention is asingle stranded nucleic acid molecule of between 8 to 500 nucleotides inlength, which can specifically hybridize to a region of a nucleic acidencoding a FOXM1 polypeptide or a CENPF polypeptide. Longerpolynucleotides encoding 250, 500, or 1000 bases and longer arecontemplated as well. Such oligonucleotides will find use, for example,as probes in Southern and Northern blots and as primers in amplificationreactions.

Conditions can be selected for hybridization where an exactlycomplementary target and probes can hybridize, i.e., each base pair mustinteract with its complementary base pair. Alternatively, conditions canbe selected where a target and probes have mismatches but are still ableto hybridize. Suitable conditions can be selected, for example, byvarying the concentrations of salt in the prehybridization,hybridization and wash solutions, by varying the hybridization and washtemperatures, or by varying the polarity of the prehybridization,hybridization or wash solutions.

Suitable hybridization conditions for the diagnostic methods are thoseconditions that allow the detection of gene expression from identifiableexpression units such as genes. Exemplary stringent hybridizationconditions include but are not limited to hybridization at 42° C. in asolution (i.e., a hybridization solution) comprising 50% formamide, 1%SDS, 1 M NaCl, 10% dextran sulfate, and washing twice for 30 minutes at60° C. in a wash solution comprising 0.1×SSC and 1% SDS. Hybridizationcan be performed at low stringency with buffers, such as 6×SSPE with0.005% Triton X-100 at 37° C., which permits hybridization betweentarget and probes that contain some mismatches to form targetpolynucleotide/probe complexes. Subsequent washes are performed athigher stringency with buffers, such as 0.5×SSPE with 0.005% TritonX-100 at 50° C., to retain hybridization of only those target/probecomplexes that contain exactly complementary sequences. Alternatively,hybridization can be performed with buffers, such as 5×SSC/0.2% SDS at60° C. and washes are performed in 2×SSC/0.2% SDS and then in 0.1×SSC.Background signals can be reduced by the use of detergent, such assodium dodecyl sulfate, Sarcosyl or Triton X-100, or a blocking agent,such as salmon sperm DNA. It is understood in the art that conditions ofstringency can be achieved through variation of temperature and buffer,or salt concentration, as described in Ausubel et al., eds., Protocolsin Molecular Biology, John Wiley & Sons (1994), pp. 6.0.3 to 6.4.10.After hybridization, the microarray can be washed to removenonhybridized nucleic acids, and complex formation between thehybridizable array elements and the target polynucleotides is detected.Modifications in hybridization conditions can be empirically determinedor precisely calculated based on the length and the percentage ofguanosine/cytosine (GC) base pairing of the probe. The hybridizationconditions can be calculated as described in Sambrook et al., eds.,Molecular Cloning: A Laboratory Manual, Cold Spring Harbor LaboratoryPress: Cold Spring Harbor, N.Y. (1989), pp. 9.47 to 9.51.

Detection of hybridization can be achieved by labeling probes or targetpolynucleotides (e.g. a nucleic acid encoding a FOXM1 polypeptide or anucleic acid encoding a CENPF polypeptide with one or more labelingmoieties. In one embodiment, the target polynucleotides are labeled witha fluorescent label, and measurement of levels and patterns offluorescence indicative of complex formation is accomplished byfluorescence microscopy (e.g. confocal fluorescence microscopy). Thelabeling moieties can include compositions that can be detected byspectroscopic, photochemical, biochemical, bioelectronic,immunochemical, electrical, optical or chemical means. The labelingmoieties include radioisotopes, such as 3H, 14C, 32P, 33P or 35S,chemiluminescent compounds, labeled binding proteins, heavy metal atoms,spectroscopic markers, such as fluorescent markers and dyes, magneticlabels, linked enzymes, mass spectrometry tags, spin labels, electrontransfer donors and acceptors, and the like. Exemplary dyes includequinoline dyes, triarylmethane dyes, phthaleins, azo dyes, cyanine dyes,and the like. Fluorescent markers that emit light at wavelengths atleast greater than 10 nm above the wavelength of the light absorbed canbe used in some embodiments. Exemplary fluorescent markers include, butare not limited to, fluorescein, phycoerythrin, rhodamine, lissamine,and C3 and C5 available from Amersham Pharmacia Biotech (PiscatawayN.J.). Labeling can also be carried out during an amplificationreaction, such as polymerase chain reactions and in vitro transcriptionreactions, or by nick translation or 5′ or 3′-end-labeling reactions.When the label may be incorporated after or without an amplificationstep, the label is incorporated by using terminal transferase or byphosphorylating the 5′ end of the target polynucleotide using, e.g., akinase and then incubating overnight with a labeled oligonucleotide inthe presence of T4 RNA ligase. Alternatively, the labeling moiety can beincorporated after hybridization once a probe/target complex has formed.Nucleotide substitutions can be performed, as well as chemicalmodifications of the probe. Such chemical modifications can beaccomplished to increase the stability of hybrids (e.g., intercalatinggroups) or to label the probe. Some examples of labels include, withoutlimitation, radioactivity, fluorescence, luminescence, and enzymaticlabeling.

In embodiments where amplification used to detect the presence of anucleic acid encoding a FOXM1 polypeptide or nucleic acid encoding aCENPF polypeptide, such methods can be based on the formation ofspecific hybrids between primers nucleic acid sequences having completeor partial complementarity to portions of a nucleic acid encoding aFOXM1 polypeptide or to portions of a nucleic acid encoding a CENPFpolypeptide, wherein the primer sequences serve to initiate nucleic acidreproduction though, for example, PCR based methodologies. Numerousnucleic acid amplification techniques known in the art, includingtraditional polymerase chain reaction (PCR), quantitative PCR (qPCR),ligase chain reaction (LCR), strand displacement amplification (SDA) andnucleic acid sequence based amplification (NASBA). These techniques canbe performed using commercially available reagents and protocols. Usefultechniques in the art encompass real-time PCR, allele-specific PCR, orPCR-SSCP. Nucleic acid primers useful for amplifying a nucleic acidencoding a FOXM1 polypeptide or nucleic acid encoding a CENPFpolypeptide include, but are not limited to primers that specificallyhybridize with a DNA encoding a FOXM1 polypeptide or nucleic acidencoding a CENPF polypeptide, or an RNA encoding a FOXM1 polypeptide ornucleic acid encoding a CENPF polypeptide.

In some embodiments, the detection is performed by sequencing all orpart of a nucleic acid encoding a FOXM1 polypeptide or a CENPFpolypeptide or by selective hybridization or amplification of all orpart of a nucleic acid encoding a FOXM1 polypeptide or a CENPFpolypeptide. In one embodiment, the sample can comprise prostate tissuesample from a subject.

Thus, in certain aspects, the diagnostic methods described hereincomprise the use of a nucleic acid primer, wherein the primer can becomplementary to and hybridize specifically to a portion of a codingsequence (e.g., gene or RNA) of a nucleic acid encoding FOXM1 or CENPFpresent in a sample form a subject having or at risk of developing acancer, such as a prostate cancer, or a malignant prostate cancer.Primers suitable for use with the methods described herein include thosethat are specific for a nucleic acid encoding FOXM1 or CENPF. By usingsuch primers, the detection of an amplification product indicates thepresence of a nucleic acid encoding FOXM1 or CENPF or the absence ofsuch. The use of such primers can also be employed to quantify therelative or absolute amount of a nucleic acid encoding FOXM1 or CENPF ina sample.

Primers suitable for use with the methods described herein, include, butare not limited to those having the sequence of SEQ ID NOs: 5, 6, 19 and20. In certain embodiments, amplification of a FOXM1 nucleic acidsequence can be performed using a primer pair of SEQ ID NO: 5 and 19. Incertain embodiments, amplification of a CENPF nucleic acid sequence canbe performed using a primer pair of SEQ ID NO: 6 and 20. One of skill inthe art will readily be able to design and synthesize primers suitablefor amplifying FOXM1 or CENPF nucleic acid sequences.

Examples of primers of this invention can be single-stranded nucleicacid molecules of about 5 to 100 nucleotides in length, or about 8 toabout 25 nucleotides in length. Exemplary primers may be 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 or more contiguous base pairs from the abovesequences will be used, although others are contemplated. Primerssuitable for use with the methods described herein can be labelledaccording to any method known in the art, including those described foruse in labeling the probes and oligonucleotides suitable for use withthe methods described herein. Labeling of primers can also be limited tolabeling methods that do not interfere with the ability of the primer tobe used for amplification purposes.

The sequence of a primer suitable for use with the methods describedherein can be derived directly from a nucleic acid encoding FOXM1 orCENPF. Perfect complementarity is useful, to ensure high specificity.However, certain mismatch can be tolerated. For example, a nucleic acidprimer or a pair of nucleic acid primers as described herein can be usedin a method for detecting the presence of or a predisposition toprostate cancer in a subject.

Amplification methods include, e.g., polymerase chain reaction, PCR (PCRProtocols: A Guide to Methods and Applications, Innis, ed., AcademicPress, N.Y., 1990 and PCR Strategies, Innis, ed., Academic Press, Inc.,N.Y., 1995; ligase chain reaction (LCR) see, e.g., Wu and Wallace, 1989;Landegren et al., 1988; Barringer et al., 1990); transcriptionamplification (see, e.g., Kwoh et al., 1989); and self-sustainedsequence replication (see, e.g., Guatelli et al., 1990); Q Betareplicase amplification (see, e.g., Smith et al., 1997), automatedQ-beta replicase amplification assay (see, e.g., Burg et al., 1996) andother RNA polymerase mediated techniques (e.g., NASBA, Cangene,Mississauga, Ontario); see also Berger et al., 1987; Sambrook; Ausubel;U.S. Pat. Nos. 4,683,195 and 4,683,202; Sooknanan and Malek, 1995. Allthe references stated above are incorporated by reference in theirentireties.

Methods for Determining Protein Expression

According to the methods described herein, the coexpression of FOXM1 andCENPF protein is defined as being elevated to a diagnostic level if theamount of FOXM1 polypeptide and CENPF polypeptide expressed or presentin a sample exceeds a defined composite score threshold. For example, incertain embodiments, a sample can be deemed to have elevated expressionof FOXM1 and CENPF by investigating immunochemical staining (e.g.immunochemical staining using antibodies specific to FOXM1 or CENPF) ofa sample from a subject, determining the percentage of the sample thatis stained and assigning a percent staining value for the sample between0% and 100%, determining an intensity for the staining and assigning astaining intensity value for the sample on a scale of 0, 1, 2, or 3, andcalculating a score by multiplying the percent staining value by thestaining intensity value, wherein a score exceeding 100% indicates thatthe FOXM1 polypeptide and CENPF polypeptide present or expressed in thesample is at an elevated level. Thus, in certain aspects, the inventiondescribed herein related to the finding that a composite score based on(a) the percentage of a sample that is stained with immunochemical (e.g.an antibody, or a composition comprising an antibody), and (b) theintensity of the stain, can be used to diagnose a subject as having anaggressive or malignant prostate cancer, having a risk of dying from aprostate cancer, and having a risk of a prostate cancer undergoingmetastasis. One of skill in the art will readily appreciate that thescoring scales described herein need not be limited to integers and mayinclude fractional values. One of skill in the art will also understandthat many variants of the composite scoring scale can be envisioned. Forexample, staining intensity can be ranked on a scale of 0 to 7 whileretaining the fidelity of the method. Similarly, staining intensity canbe ranked on a scale of 0 to 10 while retaining the fidelity of thesystem.

Detection of a polypeptide in accordance with the methods describedherein can comprise detecting the presence of FOXM1 or CENPF polypeptidesequences in samples. In certain embodiments, detection of a polypeptidecan comprise assaying for the presence of an elevated quantity FOXM1 orCENPF polypeptide in a subject prostate cancer sample as compared to acontrol (noncancerous or indolent cancer) sample. In certainembodiments, detection of a polypeptide can comprise detecting thesubcellular localization of a quantity FOXM1 or CENPF polypeptide,and/or detection of colocalization of FOXM1 and CENPF polypeptide withina cell.

A variety of methods may be used to measure FOXM1 or CENPF proteinlevels including, but not limited to, immunologically based methods suchas standard ELISA, immuno-polymerase chain reaction (immuno-PCR) (Sanoet al., 1992), immunodetection amplified by T7 RNA polymerase (IDAT)(Zhang et al., 2001), radioimmunoassay, immunoblotting, etc. Otherapproaches include two-dimensional gel electrophoresis, massspectrometry, and proximity ligation (Fredriksson et al., 2002).

In embodiments where detection of FOXM1 and CENPF is at the level ofpolypeptide expression, different types of ligands can be used, such asantibodies that specifically recognize FOXM1 or CENPF polypeptides.Thus, in certain embodiments where the methods described herein involvedetection of a FOXM1 or a CENPF polypeptide, a test sample can becontacted with an antibody specific for a FOXM1 or a CENPF polypeptideand the formation of an immune complex can be subsequently determined todetermine the presence or location of the polypeptide. Various methodsfor detecting an immune complex can be used, such as ELISA,radioimmunoassays (RIA) and immuno-enzymatic assays (IEMA).

Antibodies suitable for use with the methods described herein can bepolyclonal antibodies, a monoclonal antibodies, as well as fragments orderivatives thereof having substantially the same antigen specificity.Fragments of antibodies that are suitable for use with the methodsdescribed herein include Fab, Fab′2, or CDR regions. Derivatives ofantibodies that are suitable for use with the methods described hereininclude single-chain antibodies, humanized antibodies, orpoly-functional antibodies. An antibody specific for a FOXM1 polypeptideor a CENPF polypeptide can be an antibody that selectively binds toFOXM1 or CENPF, namely, an antibody raised against FOXM1 or CENPFpolypeptide or an epitope-containing fragment of either polypeptide.Although non-specific binding towards other antigens can occur, bindingto the target polypeptide occurs with a higher affinity and can bereliably discriminated from non-specific binding. One of skill in theart will appreciate that many methods exist for labeling antibodies formicroscopic detection in samples. Exemplary labeling methods include,but are not limited fluorescent labeling, radioactive labeling, andquantum dots.

The diagnostic methods described herein can be performed on any suitablesample which contains nucleic acids or polypeptides, including in vitro,ex vivo, or in vivo samples. Examples of samples suitable for use withthe methods described herein include prostate tissue samples, especiallysamples of prostate tumor or cancerous prostate cells from tissuebiopsies taken from a subject having prostate cancer or at risk ofdeveloping it. In one embodiment, the sample comprises a tumor tissue.In one embodiment, the sample comprises prostate tissue. In anotherembodiment, the sample is an isolated population of prostate stem cells.The sample can be collected according to conventional techniques andused directly for diagnosis or stored. The sample can be treated priorto performing the method, in order to render or improve availability ofnucleic acids or polypeptides for testing. Treatments include, forinstance, lysis (e.g., mechanical, physical, or chemical), andcentrifugation. Also, the nucleic acids and/or polypeptides can bepre-purified or enriched by conventional techniques, and/or reduced incomplexity. Nucleic acids and polypeptides can also be treated withenzymes or other chemical or physical treatments to produce fragmentsthereof. In one embodiment, the sample is contacted with reagents, suchas probes, primers, or ligands, in order to assess the presence of FOXM1or CENPF polypeptides or nucleic acids. Contacting can be performed inany suitable device, such as a plate, tube, well, or glass. In specificembodiments, the contacting is performed on a substrate coated with thereagent, such as a nucleic acid array or a specific ligand array. Thesubstrate can be a solid or semi-solid substrate such as any supportcomprising glass, plastic, nylon, paper, metal, or polymers. Thesubstrate can be of various forms and sizes, such as a slide, amembrane, a bead, a column, or a gel. The contacting can be made underany condition suitable for a complex to be formed between the reagentand the nucleic acids or polypeptides of the sample.

Diagnostic Kits

The invention also provides for diagnostic kits comprising products andreagents for detecting in a sample from a subject the presence of aFOXM1 or CENPF polypeptides or nucleic acids or FOXM1 or CENPF activity.The kits can be useful for determining whether a sample from a subjectexpresses significantly elevated levels of FOXM1 or CENF compared to thelevel expressed in normal prostate tissue. For example, the diagnostickit according to the present invention comprises any primer, any pair ofprimers, any nucleic acid probe and/or any ligand, suitable for use withthe methods described herein. The diagnostic kits according to thepresent invention can further comprise reagents and/or protocols forperforming a hybridization, amplification or antigen-antibody immunereaction. In certain embodiments, the kits can comprise nucleic acidprimers that specifically hybridize to and can prime a polymerasereaction from a nucleic acid encoding FOXM1 or a nucleic acid encodingCENPF. In some kits nucleic acids that specifically hybridize to anucleic acid encoding FOXM1 or a nucleic acid encoding CENPF wherein inan embodiment the nucleic acid is affixed to a microarray support.

Some kits include anti-FOXM1 and/or anti-CENPF antibodies or fragmentsthereof, including monoclonal and polyclonal antibodies, and secondaryantibodies that are labeled for easy detection for example with afluorophore or horseradish peroxidase enzyme. The labeling moieties caninclude compositions that can be detected by spectroscopic,photochemical, biochemical, bioelectronic, immunochemical, electrical,optical or chemical means as described herein. For example, in certainembodiments, elevated nuclear colocalization of FOXM1 and CENPF can bemicroscopic immunofluorescent colocalization, wherein the extent ofcolocalization of FOXM1 and CENPF is determined using a Pearsoncolocalization co-efficient, a Spearman colocalization coefficient, orthe like. In certain embodiments, an amount of nuclear colocalizationyielding a Spearman colocalization coefficient P value of less thanabout 1.3×10-11 indicates that the sample is from a subject having aprostate cancer that has undergone, or is at risk of undergoingmetastasis. In certain embodiments, an amount of nuclear colocalizationyielding a Spearman colocalization coefficient P value of less thanabout 6.2×10-10 indicates that the sample is from a subject having aprostate cancer that has undergone, or is at risk of undergoingmetastasis. In certain embodiments, an amount of nuclear colocalizationyielding a Spearman colocalization coefficient P value of less thanabout 2.2×10-6 indicates that the sample is from a subject at risk ofdying from a prostate cancer. In certain embodiments, an amount ofnuclear colocalization yielding a Spearman colocalization coefficient Pvalue of less than about 3.5×10-5 indicates that the sample is from asubject at risk of dying from a prostate cancer

4. EXAMPLES 4.1 Example 1 Experimental Procedures

Pilate perturbagen studies were performed to evaluate optimal dosage andscheduling. As an example, rapamycin treatment of NP mice involvedtreating mice for 1, 2, or 5 days and concentrations varied from 10, 25and 50 mg/kg. Following treatment, expression profiling was done onprostate tumors to evaluate the dose and schedule that produced theoptimal range of gene expression changes. The number of differentiallyexpressed genes at different p-value thresholds (0.01 or 0.05) with orwithout a 1.5 fold change (FC) cut-off was determined. The perturbagenstudies were used for assembly of the mouse prostate cancer interactome.

The primary gene expression profile dataset used for ARACNe-basedreverse engineering was Taylor et al (GSE21034), which consists ofprimary human prostatectomy samples, adjacent normal tissue, andmetastases arrayed on a Affymetrix human Exon 1.0 ST microarray platform(Taylor et al., 2010). Additional expression profile datasets used were:(i) Yu et al (GSE6919): primary human prostatectomy samples and adjacentnormal tissue arrayed on a Affymetrix U95a, U95b and U95c microarrayplatforms; (ii) Wang et al (a) (GSE17951): primary human prostatectomysamples, prostate biopsies, and normal prostate arrayed on a AffymetrixU133Plus2.0 platform; (iii) Wang et al (b) (GSE8218): primary humanprostatectomy samples and normal prostate on a Affymetrix U133Aplatform; and (iv) Balk (GSE32269): biopsies of primary tumors fromsubjects with hormone-naïve prostate cancer and of bone marrow withconfirmed tumor content from subjects with metastatic castrationresistant prostate cancer (CRPC) on Affymetrix U133A platform. Availableclinico-pathological information is provided in the table of FIG. 5.

For expression profiling analyses, prostate tumors were macrodissected,and the content of tumor/cellular atypia was verified by H&E analyses.Total RNA was isolated from prostate tissues/tumors using a MagMAX-96total RNA isolation kit and biotin-labeled using the Illumina TotalPrepRNA Amplification Kit (Life Technologies). The resulting cRNA washybridized on mouseWG-6 v2 BeadArrays (Illumina). Slides were scannedusing an iScan (Illumina) and the resulting files were uploaded andbackground-corrected in BeadStudio 3.1.3.0 (Illumina, Inc.). Expressionprofiling data were normalized using standard variance stabilizingtransformation (VST) and robust spline normalization (RSN) with lumiTand lumiN functions from lumi library, in R-system v2.14.0 (The RFoundation for Statistical Computing, ISBN 3-900051-07-0). The raw andnormalized data files are deposited in Gene Expression Omnibus (GEO),accession number GSE53202

Immunostaining was performed as described in Irshad et al., 2013,Immunostaining for FOXM1 or CENPF was performed on adjacent sections ofeach TMA slide. For immunofluorescent staining on cells in culture,1×10⁵ cells infected with the experimental or control shRNA were seededin triplicate and grown in culture slides (BD Biosciences) for threedays in the presence of 0.5 μg/ml of doxycycline. Cells were washed withPBS, fixed in ice cold acetone and permeabilized in 0.25% Triton X-100in PBS and stained with antibodies for FOXM1 and CENPF (see Table 2,below). Images of the cellular localization of FOXM1 and CENPF wereobtained using a Leica TCS SP5 spectral confocal microscope. Proteinlevels were determined by percent of staining (i.e. from 0 to 100%) andintensity level of staining (i.e., 0, 1, 2, or 3) in each tumor sample.We defined a composite protein level by multiplying percent of stainingand its intensity level for each tumor sample, for FOXM1 or CENPF.Composite protein level exceeding 100 were considered elevated.

Statistical analysis was performed with survcomp package using Rv2.14.0. Cox proportional hazard model was estimated with the sury andcoxph functions. Kaplan-Meier survival analysis was performed usingsurv, survfit, and survdiff functions. Concordance indexes (c-index)were estimated and compared using coxp and concordance.index (countingties) and cindex.comp functions.

Predicting additive effects by extrapolating individual effects ofsilencing FOXM1 or CENPF was evaluated as follows. To quantitativelyevaluate synergy versus additivity of the tumor growth rate, an estimateof an “additive” effect was projected using a log-linear regressionmodel, which assumes that the silencing of either master regulatorindividually induces a fractional reduction in tumor growth from that ofcontrol mice. The difference between the projected “additive” modelversus the actual observed consequence of co-silencing was calculatedusing a one sample t-test

MARINa was used to estimate the activity levels of FOXM1 and CENPF,based on their ARACNe-inferred transcriptional targets, for each sample(i.e., each subject) in the Sboner and Glinsky human prostate cancerdatasets (FIG. 5) (Glinsky et al., 2004; Sboner et al., 2010). Theactivity was defined as elevated if activated targets were positivelyenriched in the sample signature (i.e., positive NES) and at the sametime repressed targets were negatively enriched in the sample signature(i.e., negative NES) and these enrichment scores fell into theupper/lower 35% percentile of NES distribution. Subjects were thendivided into four groups: (i) those with non-elevated inferred activityfor FOXM1 and CENPF; (ii) those with elevated inferred activity only forFOXM1; (iii) those with elevated inferred activity only for CENPF; and(iv) those with elevated inferred activity for both FOXM1 and CENPF. Forthese and all subsequent analyses, association with disease outcome wasevaluated using Kaplan-Meier survival analysis calculated along with thelog-rank p value using Surv, survfit, and survdiff functions fromsurvcomp package in R v 2.14.0.

Gene silencing of FOXM1 and CENPF as well as forced expression of FOXM1were done using lentiviral shRNAs or expression vectors (Open Biosystemsand CCSB Human ORFeome Library, respectively). Functional studies weredone in four independent human cancer cell lines, which were obtainedfrom ATCC. All experiments using animals were performed according toprotocols approved by the Institutional Animal Care and Use Committee(IACUC) at Columbia University Medical Center.

Silencing was performed using the pTRIPZ lentiviral vector (OpenBiosystems), which express an shRNAmir (microRNA-adapted shRNA,hereafter referred to as shRNA) and, for functional analysis, a tRFPfluorescent reporter under the control of a tetracycline responsiveelement (TRE) promoter such that expression of the shRNA can be inducedby addition of doxycycline (0.5 μg/ml). For two-color fluorescenceanalyses, which were used for selection of cells expressing twodifferent shRNA, the pTRIPZ vector was engineered to express eGFP usingthe AgeI and ClaI sites to replace the tRFP cassette. Followinginduction with doxycycline, cells infected with the pTRIPZ-RFP virus aredetected by RFP expression (red), those infected with the pTRIPZ-GFPvirus by GFP expression (green), and those infected with bothpTRIPZ-RFP/pTRIPZ-GFP virus by expression of both tRFP and the eGFP(yellow). The shRNAs used to silence FOXM1 and CENPF were purchased fromOpen Biosystems; sequences are provided in Table 1. Unless otherwiseindicated, analyses were done using two alternative shRNA andco-silencing was done using each combination of the experimental orcontrol shRNA lentivirus.

TABLE 1 Sequences of shRNA and Primers used for this study Purpose and Sequence name SEQ shRNA Clone ID ID Mature antisense FOXM1 shRNA#1V3THS_283849  1 ATAATTAGAGGATAATTTG FOXM1 shRNA#2 V3THS_396941  2TGATGGTCATGTTCCGGCG CENPF shRNA#1 V2THS_115502  3 ATCTGATTCACTCAGTCTGCENPF shRNA#2 V2THS_115504  4 TTTCTTCCAACAGTAACTG Scramble shRNA RHS4743N/A SEQ SEQ ID Forward ID Reverse Real Time qPCR FOXM1  5CGTCGGCCACTGATTCTCAAA 19 GGCAGGGGATCTCTTAGGTTC CENPF  6CTCTCCCGTCAACAGCGTTC 20 GTTGTGCATATTCTTGGCTTGC BRCA1  7GCTCGTGGAAGATTTCGGTGT 21 TCATCAATCACGGACGTATCATC BUB1  8AAATGACCCTCTGGATGTTTGG 22 GCATAAACGCCCTAATTTAAGCC KI67  9GGGCCAATCCTGTCGCTTAAT 23 GTTATGCGCTTGCGAACCT CYCLIN 10CGCTGGCGGTACTGAAGTC 24 GAGGAACGGTGACATGCTCAT TIMELESS 11TCTGATCCGCTATTTGAGGCA 25 GGCAGAAGGTCGCTCTGTAG CDC25 12ACGCACCTATCCCTGTCTC 26 CTGGAAGCGTCTGATGGCAA TRIP13 13ACTGTTGCACTTCACATTTTCC 27 TCGAGGAGATGGGATTTGACT PLK1 14AAAGAGATCCCGGAGGTCCTA 28 GGCTGCGGTGAATGGATATTTC HMMR 15ATGATGGCTAAGCAAGAAGGC 29 TTTCCCTTGAGACTCTTCGAGA MYBL2 16CCGGAGCAGAGGGATAGCA 30 CAGTGCGGTTAGGGAAGTGG ACTIN 17GTCTGCCTTGGTAGTGGATAATG 31 TCGAGGACGCCCTATCATGG GAPDH 18TGTGGGCATCAATGGATTTGG 32 ACACCATGTATTCCGGGTCAAT ChIP FOXM1 33CCGGAGCTTTCAGTTTGTTC 41 CGGAATGCCGAGACAAGG CENPF 34CACCTCCAGTAGAGGGGCTTG 42 TACCTCCACGCCTATTGGTC AURKA 35AGGACAAGGGCCTTCTTAGG 43 TAGTGGGTGGGGAGACAGAC AURKB 36GGGGTCCAAGGCACTGCTAC 44 GGGGCGGGAGATTTGAAAAG BIRC5 37CCATTAACCGCCAGATTTGA 45 TGTAGAGATGCGGTGGTCCT CDC25 38AAGAGCCCATCAGTTCCGCTTG 46 CCCATTTTACAGACCTGGACGC PLK1 39CCAGAGGGAGAAGATGTCCA 47 GTCGTTGTCCTCGAAAAAGC CYCLIN B2 40TCCTTTGCCGAAAGCTAGAG 48 GCAACTGCCAATCTGAAAAAG

Lentiviral particles were made using the 2nd generation packagingvectors, psPAX2 and pMD2.G (Addgene) in HEK-293T cells (ATCC), andconcentrated using the Lenti-X Concentrator reagent (Clonetech). Humanprostate cancer cells used in this study were DU145, PC3, LNCaP, and22Rv1 (ATCC). Following infection with the lentiviruses, cells wereselected using 4 μg/ml of puromycin for three days, following whichshRNA expression was induced by addition of 0.5 μg/ml of doxycycline.The optimal time-point for silencing was determined to be 72 h followinginduction and used for all analyses, unless otherwise indicated. Forenrichment of shRNA-expression, single-cell suspensions of the inducedcells were FACS sorted on a BD-FACSAria cell sorter (BD biosciences)using the FITC (emission wavelength 525 nm, GFP positive) and/or PE-A(627-702 nm emission wavelength, RFP positive) channels and cells havingthe 20% highest-level expression were collected and used for analyses.Silencing of FOXM1 and CENPF RNA and protein were confirmed by qPCR andwestern blot analyses, respectively. Sequences of primers used for realtime PCR are provided above in Table 1; antibodies are described inTable 2, with antibodies for immunohistochemistry indicated by theinitials IHC.

TABLE 2 Antibodies used in this study Description Source Type DilutionUse FOXM1 Abcam, Ab550066 Mouse 1:1000 Western (human) monoclonal blot,IF FOXM1 Abcam, Ab550066 Mouse 1:400  IHC (human) monoclonal CENPFAbcam, Ab5 Rabbit 1:200  Western (human) polyclonal blot, IF CENPFAbcam, Ab90 Mouse 1:400  IHC (human) monoclonal pAKT Cell Signaling#9271 Rabbit 1:1000 Western polyclonal blot pERK Cell Signaling #9101Rabbit 1:500  Western polyclonal blot pS6 Cell Signaling #2211 Rabbit1:1000 Western polyclonal blot Actin Cell Signaling 4970 Rabbit 1:2000Western polyclonal blot PARP Cell Signaling #9542 Rabbit 1:1000 Westernpolyclonal blot V5 Invitrogen #R96025 Mouse 1:5000 Western monoclonalblot, ChIP V5 Sigma #A7345 Mouse 0.2 μg IP monoclonal

Determining mRNA expression of FOXM1 or CENPF or both, total RNA wasisolated from lentiviral-infected cells using TRIZOL and hybridized onIllumina Human HT-12 v4 Expression BeadChip Arrays. Hybridization andexpression data processing were done as described above. Differentialgene expression analysis was estimated with student t-test using p<0.05as significant.

4.2 Example 2 Method of Discovery Gene Profiling

The t-Distributed Stochastic Neighbor Embedding (t-SNE) is a machinelearning algorithm for dimensionality reduction. It is a nonlineardimensionality reduction technique that is particularly well suited forembedding high-dimensional data into a space of two or three dimensions,which can then be visualized in a scatter plot. Specifically, it modelseach high-dimensional object by a two- or three-dimensional point insuch a way that similar objects are modeled by nearby points anddissimilar objects are modeled by distant points. In the illustratedembodiment, the gene expression data is reduced to the two dimensions V1and V2. The gene expression data was divided into 6 classes associatedwith normal cells adjacent to a tumor (AdjN), four Gleason scores (G6,G7, G8, and G9 or more), and metastasized (met). This analysis was doneto evaluate the relative heterogeneity of human and mouse datasets usedto assemble the prostate cancer interactomes. FIG. 1A depicts t-SNEanalysis of the Taylor dataset relative to Gleason score. Each point isthe two dimensional representation of the relative expression of manygenes. The 26,445 genes are considered in the t-SNE analysis. Each pointis the two dimensional representation of the similarity and divergencebetween the data sample (i.e., gene expression profile) and all otherdata samples.

Interactomes

Regulatory networks (interactomes) for human and mouse prostate cancerwere generated using the Algorithm for the Reconstruction of AccurateCellular Networks (ARACNe) (Basso et al., 2005; Margolin et al., 2006b).

ARACNe is an unbiased algorithm that infers transcriptional interactionsby computing the mutual information between each transcriptionalregulator (transcription factors and co-factors) and its potentialtargets, and then by removing indirect interactions using the DataProcessing Inequality (DPI). For optimal analysis, ARACNe requires largedatasets of gene expression profiles (≧100) having significantendogenous (i.e., genetic) and/or exogenous (i.e., perturbation-induced)heterogeneity. Thus ARACNe analysis was performed on the Taylor dataset.

ARACNe was run independently on the human and mouse datasets using aconservative mutual information threshold (p≦1.0×10⁻⁹, e.g., p≦0.05,Bonferroni corrected for all candidate interactions). This resulted inhighly robust regulatory networks in which the human interactomerepresented 249,896 interactions between 2,681 transcriptionalregulators and their inferred target genes, while the mouse interactomerepresented 222,787 interactions for 2,072 transcriptional regulators.

FIG. 2A is a block diagram and graph that illustrates exampleinteractomes for human and mouse models with prostate cancer, accordingto an embodiment. ARACNE sub-networks from the human and the mouseprostate cancer interactomes highlight selected conservedtranscriptional regulators. The scaled size of the transcriptionalregulator nodes (filled circles) indicates the level of conservationwhile the relative distance between them approximates the strength oftheir association.

The suitability of these mouse and human interactomes for cross-speciesinterrogation was next evaluated by developing a novel computationalapproach to assess the global conservation of their transcriptionalprograms.

A quantitative metric was developed to compare conservation of the humanand mouse interactomes. In particular, a modification of the MARINaalgorithm was developed that allows for single-sample analysis to inferthe differential activity of 2028 transcriptional regulators representedin both interactomes on a sample-by-sample basis, from the expression oftheir interactome-specific targets. The analysis was performed on 1009expression profiles representing 4 human datasets listed in the Table ofFIG. 5, as well as across the mouse datasets, to determine whether theactivity of each regulator, inferred either from the expression of itshuman interactome targets or its murine interactome targets, wassignificantly correlated (p≦0.05), indicating that the murine and humanregulatory programs were therefore conserved. The accuracy of thismetric was demonstrated by comparing two equivalent same-speciesinteractomes from the human and mouse datasets (i.e., positive control),in which virtually all transcriptional regulators were conserved (>90%),contrasting with randomized interactomes (i.e., negative control) thathad virtually no conservation. Histogram (density plots) showed thedistribution of the correlation coefficients of activity profiles oftranscriptional regulators for randomized interactomes (negativecontrol) and the positive control interactomes for human and mouse. Thedegree of correlations was measured by the Z-score, and the Spearmancorrelation coefficient. The Z-score, also called the standard score, isthe (signed) number of standard deviations an observation or datum isabove the mean; and, is useful in comparing different populations. TheSpearman's rank correlation coefficient, also called Spearman's rho, isa nonparametric measure of statistical dependence between two variables.It assesses how well the relationship between two variables can bedescribed using a monotonic function. If there are no repeated datavalues, a perfect Spearman correlation of +1 or −1 occurs when each ofthe variables is a perfect monotone function of the other.

Using these metrics, it was found that 70% of the transcriptionalregulators in the human and mouse prostate cancer interactomes regulatestatistically conserved programs (p≦0.05). FIG. 2B is a graph thatillustrates example percentage of the interactomes that are conservedbetween human and mouse models with prostate cancer, according to anembodiment. This histogram shows the distribution of the Z-scores forconservation of activity profiles between the human and mouseinteractomes at p≦0.05. Comparison of the androgen receptor (AR)activity levels in each sample from Taylor et al and the mouse datasetwas performed using the Spearman correlation coefficient.

Notably, conserved transcriptional regulators included many genes knownto play important roles in prostate cancer, such as AR, ETS1, ETV4,ETV5, STAT3, MYC, BRCA1, and NKX3.1. In particular, AR displayedextensive correlation of its transcriptional activity between the humanand mouse interactomes, consistent with its known role as a keyregulator of prostate development and prostate tumorigenesis.

Master Regulators

The Master Regulator Inference algorithm (MARINa) (Carro et al., 2010;Lefebvre et al., 2010) was then used to infer candidate masterregulators (MRs) that act individually or synergistically to drivemalignant prostate cancer in the conserved interactomes. MARINaestimates differential activity (DA) based on enrichment (differentialexpression, DE) of their activated and repressed targets in themalignancy signature. More specifically, MARINa identified candidate MRsbased on the concerted differential expression of their ARACNe-inferredtargets (i.e., their differential activity, DA). Specifically,“activated” MRs have positively-regulated and repressed targetssignificantly enriched among upregulated and downregulated genes,respectively, while “repressed” MRs have the converse.

To interrogate the human prostate cancer interactome, a gene signaturewas used representing prostate cancer malignancy from the Taylordataset, which compares aggressive prostate tumors (Gleason score ≧8with rapid biochemical recurrence; sample size n=10) versus indolentones (Gleason score 6 tumors with no biochemical recurrence; sample sizen=39). These analyses identified 175 candidate MRs, including 49activated and 126 repressed (p≦0.05).

To investigate the robustness of these MRs, MARINa was performed using asecond, independent malignancy signature from the Balk dataset (see thetable of FIG. 5), which compares lethal CRPC (sample size n=29) withindolent, hormone-naïve prostate cancer (sample size n=22). Theseindependent MR analyses significantly overlapped with those identifiedfrom the Taylor malignancy signature (36 MRs in common; Fisher exacttest p<0.0001). The Fisher exact test was used to compare twopopulations with the same number of members and determine theprobability p that deviations from the null hypothesis, here that thetwo distributions are the same could be explained by random events.Furthermore, MARINa analyses of 15 independent interactomes using theTaylor human prostate cancer malignancy signature showed that theinferred MRs were highly overlapping with those inferred from twoadditional independent prostate cancer interactomes (p<7×10⁹ andp<8×10⁻²⁰, Fisher exact test) but not with MRs inferred fromnon-prostate cancer specific interactomes (13 orders of magnitudedifferent in significance). Thus, inference of master regulators ofhuman prostate cancer malignancy required a prostate cancer-specificinteractome but was independent of the specific dataset used for itsinterrogation.

To identify a corresponding mouse malignancy signature, MARINa wasperformed on four independent GEMM signatures, which are associated withprostate cancer malignancy and represent the diverse range of prostatecancer phenotypes represented among the GEMMs, including the NPK, NPB,NP, NP-AI, Myc, and NP53 mouse models. Meta-analyses of independent MRlists from these four independent GEMM signatures led to theidentification of 229 candidate mouse MRs, including 110 activated and119 repressed MRs (p≦0.001).

Conserved MRs were More Likely to be Associated with Disease Outcomethan the Non-Conserved Ones

The resulting independent lists of human and mouse MRs were thenintegrated to produce a ranked list of 20 conserved MRs, including 7activated and 13 repressed (joint p-value: p≦0.0074 by Stouffer'smethod). FIG. 3A is a Venn diagram and table that illustrates exampleselection of a subset of master regulators from a full set determined byavailable automated computer processes, according to an embodiment.Notably, these conserved MRs were more likely to be associated withdisease outcome than the non-conserved ones, as assessed by a univariateCOX proportional hazard regression model (43% versus 21%; p≦0.05), andwere also more likely to be differentially expressed in aggressiveprostate tumors (metastatic versus non-metastatic; 100% versus 60%).

Subsequent analysis focused on the subset of activated conserved MRs,each of which has been associated with cancer-related biologicalprocesses: CHAF1A (chromatin activity); TRIB3 (regulation of cellsignaling in transcriptional control); FOXM1 (cell cycle progression);CENPF (mitosis); PSRC1 (growth control); TSFM (translationalelongation); and ASF1B (regulation of nucleosome assembly). FIG. 3B is adiagram that illustrates example ranking of activated master regulatorsfor their impacts on prostate cancer, according to an embodiment.Conserved activated MRs are shown for the human (left) and mouse (right)malignancy signatures, depicting the different positive (activated;upper bars) and negative (repressed; lower bars) targets. The ranks ofdifferential activity (DA) and differential expression (DE) are shown bythe shaded boxes; the numbers indicate the rank of the DE in thesignature. Differential expression is defined by comparing expressionlevels of a gene between two groups of samples (here, aggressive andindolent prostate cancer samples) using the t-test. Genes ranked (i.e.,sorted from the most over-expressed to the most under-expressed) bytheir differential expression define a signature. For example, 411represents a higher position in the signature and thus a strongerdifferential expression, compared to 13323.

FIG. 3C is a table that illustrates example ranking of master regulatorsfor their impact on prostate cancer by various available algorithms,according to an embodiment. In this summary of conserved MRs are shown:joint p-value from human and mouse MARINa analysis, calculated usingStouffer's method; p-value for COX proportional hazard regression modelapplied to mRNA expression levels and predicted MR activity; and averagep-values for differential expression of MRs in metastatic versusnon-metastatic primary tumors. Smaller p values means that thedeviations from the null hypothesis, that the regulator is notimportant, are less likely due to chance and thus the correspondingregulator is more significant contributors. FOXM1 and CENPF aresignificant (p<0.05) for all measures.

Synergistic Master Regulators FOXM1 and CENPF are DifferentiallyExpressed in Aggressive Prostate Tumors

These MRs were further prioritized by computationally evaluating theirpotential synergistic interactions. By these criteria, any pair of MRswas considered “synergistic” if their co-regulated ARACNe-inferredtargets were significantly more enriched in the malignancy signaturethan their individual targets (p≦0.001) (Carro et al., 2010; Lefebvre etal., 2010). Using this computational approach to analyze all 21 possiblepairs among the conserved activated MRs, the only pair that was found tobe statistically significant was FOXM1 and CENPF.

FIG. 4 is a table that illustrates example predicted synergy of FOXM1and CENPF among other pairs in the subset of master regulators usingavailable algorithms, according to an embodiment. Shown are synergyp-values (i.e., enrichment of shared versus non-shared targets in themalignancy signature) for conserved MRs, inferred by MARINa. Clearly,the synergy of FOXM1 and CENPF is least likely to be random (p<0.001),and thus most significant.

5. ALTERNATIVES AND EXTENSIONS

In the foregoing specification, the invention has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the invention. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. Throughout thisspecification and the claims, unless the context requires otherwise, theword “comprise” and its variations, such as “comprises” and“comprising,” will be understood to imply the inclusion of a stateditem, element or step or group of items, elements or steps but not theexclusion of any other item, element or step or group of items, elementsor steps. Furthermore, the indefinite article “a” or “an” is meant toindicate one or more of the item, element or step modified by thearticle.

6. REFERENCES

Reference to the following publications can be found herein.

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What is claimed is:
 1. A method comprising: a) obtaining a test prostatecancer sample from a subject having prostate cancer; b) determining alevel of expression of each of the genes encoding (FOXM1) Forkhead boxprotein M1 and Centromere protein F (CENPF) in the test sample and acontrol sample; c) comparing the level of expression of each of theFOXM1 and CENPF genes in the test sample to the corresponding level inthe control sample; and d) if the level of expression of each of theFOXM1 and CENPF genes in the test sample is at least 35% higher than thecorresponding level in the control sample, then determining that thesubject has an aggressive form of prostate cancer or has a high risk ofprostate cancer progressing to an aggressive form.
 2. The method ofclaim 1, wherein determining the level of expression of the prognosticgenes FOXM1 and CENPF comprises determining a level of mRNA encodingFOXM1 and CENPF in the sample, respectively, using a method selectedfrom the group consisting of nuclease protection assays, northern blots,real time quantitative PCR, and in-situ hybridization.
 3. The method ofclaim 1, wherein determining the level of expression of the prognosticgenes FOXM1 and CENPF comprises determining a level of FOXM1 protein orCENPF protein in the sample, respectively, using a method selected fromthe group consisting western blots, 2-dimensional SDS-PAGE, and massspectrometry.
 4. A method comprising: a) obtaining a prostate cancersample from a subject having prostate cancer; b) determining a level ofexpression of FOXM1 protein and CENPF protein in the prostate cancersample by immunostaining with a first antibody that specifically bindsto FOXM1 and a second antibody that specifically binds to CENPF; and c)if at least 50% of prostate cancer cells in the prostate cancer sampleexpress both FOXM1 protein and CENPF protein at a composite score of atleast 100 for each protein, wherein the composite score is calculated bymultiplying a percent staining value by a staining intensity value, thendetermining that the subject has an aggressive form of prostate canceror has a high risk of prostate cancer progressing to an aggressive form.5. The method of claim 4, wherein both FOXM1 protein and CENPF proteinare colocalized in the nucleus of at least 50% of prostate cancer cellsin the sample.
 6. The method of one of claims 1 and 4, wherein theprostate cancer sample comprises circulating prostate cancer cells thathave been isolated.
 7. A method comprising: a) obtaining a prostatecancer sample from a subject having prostate cancer (or at risk ofdeveloping prostate cancer), b) applying a first antibody thatspecifically binds to FOXM1 protein in the sample, wherein presence ofFOXM1 creates an antibody-FOXM1 complex; and applying a second antibodythat specifically binds to CENPF in the sample, wherein presence of theCENPF creates an antibody-CENPF complex, c) applying a first detectionagent that detects the antibody-FOXM1 complex; and a second detectionagent that detects the antibody-CENPF complex, and d) if at least 50% ofprostate cancer cells in the sample express both FOXM1 protein and CENPFprotein at a composite score of at least 100 for each protein, whereinthe composite score is calculated by multiplying a percent stainingvalue by a staining intensity value, then determining that the subjecthas an aggressive form of prostate cancer or has a high risk of prostatecancer progressing to an aggressive form.
 8. The method as in claim 1, 4or 7, further comprising treating the subject for aggressive prostatecancer if a determination is made that the cancer is aggressive prostatecancer.
 9. The method as in claim 1, 4 or 7, wherein the controlprostate tissue sample comes from a normal subject that does not havecancer or from a noncancerous area of the subject's prostate.
 10. Adiagnostic kit for detecting an expression level of an mRNA or a proteinencoding FOXM1 or CENPF or both in a biological sample, the kitcomprising oligonucleotides that specifically hybridize to each of therespective mRNAs or one or more agents that specifically bind to each ofthe respective proteins, or both.
 11. The diagnostic kit of claim 10,further comprising a forward primer and a reverse primer specific foreach mRNA encoding FOXM1 or CENPF for use in a qRT-PCR assay tospecifically quantify the expression level of each mRNA.
 12. Thediagnostic kit of claim 10, wherein the agents comprise one or moreantibodies or antibody fragments that specifically bind to each of therespective FOXM1 or CENPF protein.
 13. A microarray comprising aplurality of oligonucleotides that specifically hybridize to an mRNAencoded by each of the FOXM1 or CENPF genes, which oligonucleotides arefixed on the microarray.
 14. The microarray of claim 13, wherein theoligonucleotides are labeled to facilitate detection of hybridization tothe mRNAs.
 15. The microarray of claim 14, wherein the oligonucleotidesare radio-labeled, or biotin-labeled.
 16. The microarray of claim 13,wherein the oligonucleotides are cDNAs.
 17. A microarray comprising aplurality of antibodies or antibody fragments that specifically bind toeither or both of FOXM1 protein or CENPF protein or biologically activefragment thereof, which antibodies or antibody fragments are fixed onthe microarray.
 18. The microarray of claim 17, wherein the antibodiesor antibody fragments are labeled to facilitate detection of binding tothe protein.
 19. The microarray of claim 18, wherein the antibodies orantibody fragments are radio-labeled, biotin-labeled,chromophore-labeled, fluorophore-labeled, or enzyme-labeled.